Pipeline run
01d626cb-24d3-4f05-9fd9-65aea91e9799
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Machine Learning Engineer
CASE Eslug: machine-learning-engineer · id: — · source: llm
The role aligns with the primary skills of C, C++, Machine Learning, and Neural Networks.
Job description
Role : Video Codec Engineer Required Experience: Candidates must have development experience ranging from 2 to 4 years. • Experience in implementing video compression standards based and/or proprietary Image and Video codecs/algorithms • Must have exposure and development experience ARM and/or x86 based platforms like Xeon E5/E3, Core-i7/i5 • Experience of development using operating systems like Windows / Linux / OS X Job Description: The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology. Responsibility: The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to): • Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs • Work involves implementation of high quality video encoders, decoders and transcoders and associated intellectual properties like Motion estimation, Rate Control algorithms, Scene Cut Detection, Fade-in / Fade-out Compensation, De-interlacing, De-noising as an example • Working on latest technology of Machine learning and Neural Network based video compression Educational Qualification: Masters or Bachelor’s Degree in Computer Science / Electronics and Communication Required Technical Skills: • Knowledge of C/C++ • Knowledge of x86 based development, intrinsic like SSE, AVX based coding • Knowledge of ARM based development, intrinsic like Neon coding • Debugging, profiling and development environments • Good knowledge of video standards like AV1 and H.265 • Working knowledge of H.264, MPEG-2 and VP9 is good to possess • Software Processes, Git, Configuration Management, Test Planning and Execution • Exposure to multi-threaded, cache optimal designs of video codecs • Exposure to OpenCL based GPU development / CUDA based programming • Aware of Machine learning and Neural Network basics. Location: Bengaluru, Karnataka
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Engineer, Full Stack Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Engineer, Full Stack Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
x86 remains a core ISA in server, desktop, and systems JDs; Intel/AMD roadmaps and widespread Linux/Windows support show sustained market demand, not replacement.
(0.95)
“x86” is a specific CPU instruction set architecture term; unlikely to be confused with other catalog skills.
Not versioned
Concept ·instruction_set_architecture confidence 0.93
x86 is fundamentally an instruction set architecture, which fits the Architecture vs Concept rule as a system-shape/technical knowledge unit rather than a language, tool, or runtime.
- Category
- Concept
- Sub-category
- instruction_set_architecture
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
x86 Architecture and Assembly
Pipeline tentative id
Low-level programming and platform knowledge for the x86 instruction set and processor architecture. This belongs here because x86 is the core CPU ISA used for assembly, ABI-level work, and performance-sensitive systems code.
Aliases — catalog
- AArch64 (VERSION)
- ARM (CANONICAL)
- ARMv8 (VERSION)
- arm64 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Instruction Set Architecture
- Confidence
- 0.91
- Version strategy
- SEPARATE_ENTITY
- Version tag
- ARMv8-A
Maturity reasoning: ARM is a dominant instruction-set architecture in mobile, embedded, and increasingly server/cloud chips; job postings commonly mention ARM64/AArch64 alongside Linux and systems work.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1222
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
SSE appears in more job descriptions for real-time web features, but it is still far less common than WebSockets; GitHub usage and framework docs have grown, yet it remains a secondary protocol choice.
(0.90)
SSE is a specific web streaming protocol (Server-Sent Events); “SSE” in JDs typically maps to that, not another catalog skill.
Not versioned
Protocol ·server_sent_events confidence 0.93
SSE (Server-Sent Events) is a communication standard for one-way event streaming over HTTP, so by the Protocol rule it is a protocol rather than a format or architecture.
- Category
- Protocol
- Sub-category
- server_sent_events
- Skill nature
- PROTOCOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Server-Sent Events
Pipeline tentative id
Browser-to-server streaming over HTTP using the SSE protocol. This belongs here because SSE is a specific mechanism for pushing incremental updates, notifications, and live data to clients.
Skill enrichment (orchestrator / LLM)
AVX appears in low-volume hardware/low-level systems JDs, usually as a bonus for SIMD optimization rather than a core requirement; market demand is far narrower than mainstream software stacks.
Intel ·unknown ·since 2011 (0.85)
AVX is a specific CPU instruction set extension; unlikely to be confused with other catalog skills in typical JDs.
Not versioned
Standard ·instruction_set_extension_standard confidence 0.90
AVX is a named CPU instruction-set specification, so by the Standard rule it is an industry-defined specification rather than a language, tool, or concept.
- Category
- Standard
- Sub-category
- instruction_set_extension_standard
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
SIMD Instruction Set Extensions
Pipeline tentative id
Low-level CPU vector instruction extensions used to accelerate data-parallel computation. AVX belongs here because it is an x86 SIMD extension used for high-throughput arithmetic and media processing.
Skill enrichment (orchestrator / LLM)
NEON appears in cloud database JDs and vendor docs as a Postgres serverless option, but JD volume is still far below AWS RDS/PostgreSQL and it’s not yet a universal hiring staple.
Neon Labs, Inc. ·apache_2 ·since 2021 (0.90)
“NEON” most commonly refers to the NEON Postgres platform; it’s not a common acronym that overlaps with other catalog skills.
Not versioned
Platform ·database_platform confidence 0.90
By the Vendor SaaS = Platform rule, NEON is the hosted multi-tenant Neon database service rather than software you run yourself, so it fits Platform.
- Category
- Platform
- Sub-category
- database_platform
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
Locked dimensions (v3 placement)
-
SIMD and Vector Intrinsics
Pipeline tentative id
Low-level vectorized programming primitives used to accelerate compute-heavy code paths with data-parallel operations. NEON belongs here because it is ARM's SIMD instruction set and intrinsic API for writing optimized media and signal-processing code.
-
Video Codec Optimization
Pipeline tentative id
Implementation and optimization techniques for efficient video encoding and decoding pipelines. NEON fits here when used to accelerate codec hot paths such as motion compensation, transforms, filtering, and pixel processing.
Skill enrichment (orchestrator / LLM)
AV1 is increasingly requested in streaming/media JDs and supported by major vendors (YouTube, Netflix, Chrome/Firefox), but it is still far less universal than H.264/H.265 in job postings.
Alliance for Open Media ·other_open ·since 2018 (0.95)
AV1 is a specific video codec standard name; unlikely to be confused with other catalog skills.
Not versioned
Standard ·video_codec_standard confidence 0.93
AV1 is fundamentally an industry-defined specification for video compression, so by the Standard rule it fits Standard rather than Format or Protocol.
- Category
- Standard
- Sub-category
- video_codec_standard
- Skill nature
- STANDARD
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Codec Standards
Pipeline tentative id
Standards and specifications for video compression formats used to encode, decode, and transport video efficiently. AV1 belongs here because it is a modern open video codec standard with defined bitstream, profiles, and interoperability requirements.
-
Video Codec Implementation
Pipeline tentative id
Implementation of video codecs in software or firmware, including encoder and decoder behavior, optimization, and conformance testing. AV1 fits here when the skill refers to building or integrating codec logic rather than only understanding the standard.
Skill enrichment (orchestrator / LLM)
H.265/HEVC is widely used in streaming and device pipelines; job postings for video/codec roles still mention HEVC alongside H.264, and major vendors ship hardware decode support.
ITU-T ·unknown ·since 2013 (0.85)
H.265 is a specific video codec identifier; JDs typically won’t confuse it with other distinct codec formats.
Not versioned
Format ·video_codec_format confidence 0.90
H.265 (HEVC) is a video compression specification that defines how media is encoded/structured, so it fits the Format category rather than a Protocol or Concept.
- Category
- Format
- Sub-category
- video_codec_format
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Codec Standards
Pipeline tentative id
Standards and specifications for compressing and decoding digital video streams. H.265 belongs here because it is a video compression codec standard used for efficient encoding, transmission, and playback.
Skill enrichment (orchestrator / LLM)
H.264 remains a standard codec in job postings for video streaming, conferencing, and media pipelines; it is widely supported by browsers, devices, and vendor docs, with no announced sunset or replacement in general use.
ITU-T ·other_open ·since 2003 (0.90)
Could be confused with: h-265
H.264 is commonly mentioned alongside H.265/HEVC in video codec contexts; JDs may refer to either without clear distinction.
Not versioned
Format ·video_codec_format confidence 0.90
H.264 is a video compression specification/bitstream format rather than software or a communication protocol, so it fits the Format type.
- Category
- Format
- Sub-category
- video_codec_format
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Codec Standards
Pipeline tentative id
Standards and specifications for compressing and decoding digital video streams. H.264 belongs here because it is a widely used video compression codec defined by the AVC standard.
Skill enrichment (orchestrator / LLM)
MPEG-2 is largely superseded in new video workflows by H.264/H.265 and AV1; recent job postings rarely list it except for legacy broadcast/DVD support, and modern vendor docs position newer codecs as the default.
Moving Picture Experts Group ·unknown ·since 1994 (0.90)
Could be confused with: mpeg-4, h-264
MPEG-2 in JDs is often mentioned alongside other video codecs/formats like MPEG-4 or H.264, and extractors may map it to the wrong codec.
Not versioned
Format ·video_compression_format confidence 0.93
MPEG-2 is an industry-defined media compression specification, so by the Format vs Standard rule it is best treated as a data/wire format rather than a protocol or concept.
- Category
- Format
- Sub-category
- video_compression_format
- Skill nature
- STANDARD
- Volatility
- DEPRECATED
- Typical lifespan
- SHORT_LIVED
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Compression Standards
Pipeline tentative id
Standards and specifications for encoding and decoding digital video streams. MPEG-2 belongs here because it is a foundational video compression format used to represent, transport, and decode compressed video content.
-
Digital Video Container Formats
Pipeline tentative id
Packaging and multiplexing formats used to store or transport compressed media streams. MPEG-2 is often encountered as part of MPEG-2 Program Stream or Transport Stream workflows, so it can sit in this packaging dimension as well.
Skill enrichment (orchestrator / LLM)
VP9 appears in some media/codec JDs and browser/video stack work, but market demand is far lower than H.264/AV1 and it is not a common hiring-pipeline staple.
(0.95)
VP9 is a specific video codec standard; typical JDs won’t confuse it with other unrelated skills in the catalog.
Not versioned
Concept ·general confidence 0.00
Stage 4 failed; fallback typed record.
- Category
- Concept
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Systems Programming Catalog dimension db id 166
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Codec Standards
Pipeline tentative id
Standards and bitstream formats for compressed video encoding and decoding. VP9 belongs here because it is a specific video codec specification used to represent and transmit video efficiently.
-
Video Codec Implementation
Pipeline tentative id
Engineering of encoder and decoder software for video compression formats. VP9 fits here when the skill is about implementing, optimizing, or integrating the codec in a media stack rather than just knowing the standard.
Aliases — catalog
- Git (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Version Control Tool
- Vendor
- Linus Torvalds
- License
- gpl_v2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 730
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
OpenCL appears in far fewer job postings than CUDA or Vulkan compute, and most new GPU compute roles specify CUDA/ROCm instead; market demand is concentrated in specialized cross-vendor HPC/embedded work.
Khronos Group ·other_open ·since 2008 (0.90)
OpenCL is a specific parallel computing standard; unlikely to be confused with other catalog skills in typical JDs.
Versioned 3.0
{
"OpenCL 3": "3.0",
"OpenCL 3.0": "3.0"
}
Standard ·parallel_computing_standard confidence 0.90
OpenCL is an industry specification for heterogeneous parallel computing, so by the Standard rule it is a standard rather than a language or framework.
- Category
- Standard
- Sub-category
- parallel_computing_standard
- Skill nature
- STANDARD
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- SEPARATE_ENTITY
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
GPU Parallel Programming
Pipeline tentative id
Programming models and APIs for writing compute workloads that run on GPUs and other accelerators. OpenCL belongs here because it defines kernels, memory transfers, and execution control for heterogeneous parallel computation.
Skill enrichment (orchestrator / LLM)
CUDA appears in many ML/HPC job postings and is the standard NVIDIA GPU programming stack; it remains the default target for GPU acceleration rather than a sunset technology.
NVIDIA ·proprietary ·since 2006 (0.95)
CUDA is a specific NVIDIA GPU programming platform; typical JDs won’t confuse it with other catalog skills.
Versioned 12.4
{
"CUDA 12": "12.4",
"CUDA 12.4": "12.4",
"CUDA 12.x": "12.4",
"cuda12": "12.4",
"cuda12.4": "12.4"
}
Language ·gpu_programming_language confidence 0.90
CUDA is fundamentally a programming language/API model for writing GPU code, so it fits the Language type rather than a tool or framework.
- Category
- Language
- Sub-category
- gpu_programming_language
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- SEPARATE_ENTITY
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
GPU Parallel Programming
Pipeline tentative id
Programming models and APIs for writing high-performance code that runs on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing GPU kernels, memory transfers, and device-side execution.
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Machine Learning
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1024
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
AI Governance and Model Security Catalog dimension db id 50
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, ML Ops Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Skill enrichment (orchestrator / LLM)
Broadly listed in ML/AI job descriptions and core to modern deep learning stacks; major frameworks like PyTorch and TensorFlow center on neural networks.
(0.95)
“Neural Networks” is a specific ML model concept; typical JDs won’t confuse it with other distinct skills in the catalog.
Not versioned
Concept ·machine_learning_model_concept confidence 0.96
Neural Networks are a named knowledge unit about model structure and learning, so by the Concept vs Methodology rule they are a Concept rather than an Architecture or Framework.
- Category
- Concept
- Sub-category
- machine_learning_model_concept
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
Locked dimensions (v3 placement)
-
Neural Network Frameworks
Reuses catalog slug
Core libraries and APIs used to define, train, and run neural network models. Neural Networks belongs here because the skill is typically expressed through model-building frameworks rather than standalone infrastructure or governance topics.
-
ML Frameworks and Libraries
Reuses catalog slug
Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.
Library artifacts (this run)
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science / Electronics and Communication",
"raw": "Masters or Bachelor\u2019s Degree in Computer Science / Electronics and Communication",
"requirement": "required"
}
],
"experience": {
"max": 4,
"min": 2,
"raw": "Candidates must have development experience ranging from 2 to 4 years."
},
"job_locations": [
{
"aliases": [
"Bangalore"
],
"city": "Bengaluru",
"country": "India",
"state": "Karnataka",
"work_mode": null
}
],
"role": "Video Codec Engineer",
"role_aliases": [
"Codec Engineer",
"Video Engineer",
"Video Compression Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Job Description",
"heading_was_present": true,
"source_marker": {
"first_5_words": "The prospective candidate will be",
"last_5_words": "in video technology."
},
"text": "The prospective candidate will be part of the Advanced Video and Research Team that designs and delivers video codec solutions for industry leaders in video technology.",
"word_count": 27
},
{
"bullet_count": 3,
"heading": "Responsibility",
"heading_was_present": true,
"source_marker": {
"first_5_words": "The key responsibilities of the",
"last_5_words": "video compression"
},
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}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"Video Engineer",
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{
"aliases": [
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}
API 2 — extract-details
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"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Broadly listed in ML/AI job descriptions and core to modern deep learning stacks; major frameworks like PyTorch and TensorFlow center on neural networks."
},
"skill_id": "neural-networks",
"vendor_license": {
"confidence": 0.95,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [
{
"a_dim_id": "ml-frameworks-and-libraries",
"a_name": "Neural Network Frameworks",
"a_role": "__skill_focal__",
"b_dim_id": "ml-frameworks-and-libraries",
"b_name": "ML Frameworks and Libraries",
"b_role": "ML Ops Engineer",
"pair_kind": "cross_role",
"reasoning": "Dim A is NN-specific: it centers on PyTorch/TensorFlow/Keras/JAX/MXNet and model layers, activations, backpropagation, and loss functions. Dim B is broader ML frameworks/libraries for defining models, training, inference, and evaluating predictive performance in an MLOps context. The overlap is mostly shared tooling names, but A is about neural-network model-building, while B can include wider ML workflows and evaluation. career-track: no, because a senior NN-framework practitioner is not automatically a senior MLOps engineer.",
"similarity": 0.6733393067801547
}
],
"locked_dimensions": [
{
"description": "Core libraries and APIs used to define, train, and run neural network models. Neural Networks belongs here because the skill is typically expressed through model-building frameworks rather than standalone infrastructure or governance topics.",
"exemplar_skills": [
"Neural Networks",
"PyTorch",
"TensorFlow",
"Keras",
"JAX",
"MXNet"
],
"in_scope": "Neural Networks, TensorFlow, PyTorch, Keras, JAX, MXNet, model layers, activations, backpropagation, loss functions, optimizers, inference APIs",
"name": "Neural Network Frameworks",
"out_of_scope": "workflow orchestration, experiment tracking, deployment platforms, model governance, data labeling, GPU cluster management",
"overlap_flags": [
{
"reason": "Neural network training often uses the same tools, but this dimension is about model definition and execution rather than run comparison or metrics management.",
"with_dim_id": "experiment-tracking-and-evaluation",
"with_dim_name": null,
"with_role": "ML Engineer, ML Ops Engineer"
}
],
"tentative_id": "ml-frameworks-and-libraries"
},
{
"description": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
"exemplar_skills": [
"ML Frameworks and Libraries"
],
"in_scope": "Skills, tools, and practices that belong under ML Frameworks and Libraries for the target role, including items implied by the dimension rationale.",
"name": "ML Frameworks and Libraries",
"out_of_scope": "Adjacent clusters explicitly not owned by ML Frameworks and Libraries, including unrelated platforms, roles, and skill families per library policy.",
"overlap_flags": [],
"tentative_id": "ml-frameworks-and-libraries"
}
],
"merge_log": [],
"placed": {
"name": "Neural Networks",
"placement_confidence": 0.92,
"primary_dimension": "ml-frameworks-and-libraries",
"reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "neural-networks"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"tensorflow",
"pytorch",
"scikit-learn",
"vision-language-models",
"multimodal-document-understanding",
"anomaly-detection",
"agentic-systems",
"ai-infrastructure"
],
"requires": [],
"skill_id": "neural-networks",
"suppress_on_match": []
},
"skill_id": "neural-networks",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.96,
"name": "Neural Networks",
"reasoning": "Neural Networks are a named knowledge unit about model structure and learning, so by the Concept vs Methodology rule they are a Concept rather than an Architecture or Framework.",
"skill_id": "neural-networks",
"subtype": "machine_learning_model_concept",
"type": "Concept"
},
"warnings": [
"stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"x86",
"SSE",
"AVX",
"NEON",
"AV1",
"H.265",
"H.264",
"MPEG-2",
"VP9",
"OpenCL",
"CUDA",
"Neural Networks"
]
}
API 3 — final-role-output
{}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.