Pipeline run
e79f432c-cf5c-4ad5-848c-ebe534d2304b
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Video Codec Engineer
slug: video-codec-engineer · id: — · source: llm
The primary skills indicate a strong focus on video codec development using languages like C and C++ along with machine learning techniques.
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 dominant ISA in server/desktop JDs and vendor roadmaps; Intel and AMD continue active platform support, with widespread Linux/Windows deployment and toolchain compatibility.
(0.90)
“x86” specifically denotes the x86 instruction set architecture; it’s unlikely to be confused with other distinct ISA/architecture skills in typical JDs.
Not versioned
Architecture ·instruction_set_architecture confidence 0.97
x86 is fundamentally an instruction set architecture, so it fits the Architecture type rather than a language, tool, or runtime.
- Category
- Architecture
- Sub-category
- instruction_set_architecture
- Skill nature
- PATTERN
- 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)
-
x86 Architecture and Assembly
Pipeline tentative id
Low-level x86 CPU architecture knowledge used to write, read, and optimize machine code. This fits the target skill because x86 is the instruction-set and execution model underlying assembly-level performance work, binary compatibility, and codec hot paths.
-
SIMD and CPU Intrinsics
Pipeline tentative id
Vectorized CPU programming techniques for accelerating compute-heavy workloads on x86 processors. This belongs here when x86 is used as the target platform for SSE, AVX, and intrinsic-based optimization in media and codec pipelines.
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)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
No v3 new_skill_meta for this skill (orchestrator skipped or failed).
Skill enrichment (orchestrator / LLM)
AVX appears in low-volume systems/embedded/HPC job postings and compiler/CPU optimization docs, but is rarely a standalone hiring requirement versus broader SIMD/C++ performance skills.
Intel ·since 2011 (0.90)
AVX is a specific CPU instruction set extension; typical JDs won’t confuse it with other unrelated skills.
Not versioned
Standard ·instruction_set_extension_standard confidence 0.90
AVX is an industry-defined CPU instruction set specification, so by the Standard rule it is a standard 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 Vector Instruction Sets
Pipeline tentative id
Low-level CPU instruction set extensions used to process multiple data elements in parallel. AVX belongs here because it is an x86 SIMD extension used for performance-critical media, numeric, and signal-processing code.
Skill enrichment (orchestrator / LLM)
NEON appears increasingly in cloud database JDs and vendor docs as a serverless Postgres platform, but it is far from universal compared with AWS RDS/PostgreSQL; market signal is growing job-listing adoption rather than broad staple status.
Neon Labs ·apache_2 ·since 2021 (0.90)
“NEON” as a database platform is specific; typical JDs won’t confuse it with other common skills in the catalog.
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)
Locked dimensions (v3 placement)
-
SIMD and Vector Intrinsics
Pipeline tentative id
Low-level CPU vectorization APIs and instruction-set intrinsics used to accelerate media, signal-processing, and numerical code. NEON belongs here because it is ARM's SIMD extension for writing data-parallel operations directly against the processor.
Skill enrichment (orchestrator / LLM)
AV1 is increasingly requested in streaming/media JDs and supported by major vendors (YouTube, Netflix, Chrome/Firefox), but H.264/H.265 still dominate many production pipelines.
Alliance for Open Media ·other_open ·since 2018 (0.95)
Could be confused with: h264, hevc
AV1 is a video codec acronym; JDs may mention codecs generically or confuse it with other common codecs like H.264/HEVC.
Not versioned
Format ·video_codec_format confidence 0.90
AV1 is fundamentally a video compression specification/bitstream format rather than a software product, so it fits the Format type.
- Category
- Format
- Sub-category
- video_codec_format
- 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 compressed video formats used in encoding, decoding, and streaming pipelines. AV1 belongs here because it is a specific modern video codec standard rather than a general media tool.
-
Video Encoding and Transcoding
Pipeline tentative id
Implementation of media processing pipelines that encode, decode, transcode, and optimize video assets for delivery. AV1 belongs here when the skill is used in practical encoder integration, pipeline tuning, or format conversion work.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
No v3 new_skill_meta for this skill (orchestrator skipped or failed).
Skill enrichment (orchestrator / LLM)
H.264 remains a default video codec in job specs for streaming, conferencing, and media pipelines, and is still broadly supported by major vendors and browsers; market demand is sustained despite newer codecs.
ITU-T ·unknown ·since 2003 (0.85)
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, so by the Format rule it is a data/wire representation rather than a software system or protocol.
- 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; current job postings rarely list it except for legacy broadcast/DVD systems.
Moving Picture Experts Group ·unknown ·since 1994 (0.90)
Could be confused with: mpeg-4, h-264, hevc
MPEG-2 is a video compression standard and JDs may mention generic MPEG/codec terms that could be extracted as other common codecs/formats.
Versioned 2
{
"MPEG 2": "2",
"MPEG-2": "2",
"MPEG2": "2"
}
Format ·video_compression_format confidence 0.90
MPEG-2 is an industry-defined media encoding specification, so by the Format vs Standard rule it is best treated as a format rather than a tool or concept.
- Category
- Format
- Sub-category
- video_compression_format
- Skill nature
- STANDARD
- Volatility
- DEPRECATED
- Typical lifespan
- SHORT_LIVED
- Version strategy
- SEPARATE_ENTITY
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Video Compression Standards
Pipeline tentative id
Standards and codecs used to compress, encode, and decode digital video for storage, broadcast, and streaming. MPEG-2 belongs here because it is a foundational video compression format with defined bitstream syntax and interoperability requirements.
Skill enrichment (orchestrator / LLM)
VP9 appears in some media/streaming JDs and browser/video tooling, but job-posting volume is far below H.264/AV1 and it is not a common hiring-pipeline staple.
Google ·bsd ·since 2013 (0.95)
“VP9” is a specific video codec format name; unlikely to be confused with other catalog skills.
Not versioned
Format ·video_codec_format confidence 0.90
VP9 is fundamentally a video compression specification/bitstream format rather than a language, tool, or datastore, 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
Compression standards and bitstream formats used to encode and decode digital video. VP9 belongs here because it is a specific video codec standard with defined syntax, profiles, and interoperability requirements.
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)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
No v3 new_skill_meta for this skill (orchestrator skipped or failed).
Skill enrichment (orchestrator / LLM)
CUDA appears in many ML/HPC job postings and is the standard NVIDIA GPU programming stack; vendor docs and ecosystem tooling (cuDNN, TensorRT) reinforce broad adoption.
NVIDIA ·proprietary ·since 2006 (0.95)
CUDA is a specific NVIDIA GPU programming platform/language; typical JDs use it unambiguously versus other skills.
Versioned 12.x
{
"CUDA 12": "12.x",
"CUDA 12.x": "12.x",
"cuda 12": "12.x",
"cuda12": "12.x"
}
Language ·gpu_programming_language confidence 0.90
CUDA is fundamentally a programming language/toolchain for writing GPU kernels and host-device code, so it fits the Language type rather than a library 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 compute kernels that run efficiently on GPUs. CUDA belongs here because it is the primary NVIDIA platform for expressing parallel execution, memory movement, and device-side optimization.
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)
Neural networks are a core ML concept widely listed in job descriptions for ML/AI roles and underpin mainstream frameworks like PyTorch and TensorFlow, indicating broad market adoption.
(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 0.97
Neural Networks are a named knowledge unit in machine 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 as a standalone infrastructure concern.
-
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",
"Software 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"
},
"text": "The key responsibilities of the job would be to deliver and excel on the following fronts (not limited to):\n\u2022 Development and implementation of optimized algorithms for video encoders, video decoders, video pre and post processing components on x86 and ARM based CPUs\n\u2022 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\n\u2022 Working on latest technology of Machine learning and Neural Network based video compression",
"word_count": 64
},
{
"bullet_count": 10,
"heading": "Required Technical Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Knowledge of C/C++\n\u2022 Knowledge of",
"last_5_words": "and Neural Network basics."
},
"text": "\u2022 Knowledge of C/C++\n\u2022 Knowledge of x86 based development, intrinsic like SSE, AVX based coding\n\u2022 Knowledge of ARM based development, intrinsic like Neon coding\n\u2022 Debugging, profiling and development environments\n\u2022 Good knowledge of video standards like AV1 and H.265\n\u2022 Working knowledge of H.264, MPEG-2 and VP9 is good to possess\n\u2022 Software Processes, Git, Configuration Management, Test Planning and Execution\n\u2022 Exposure to multi-threaded, cache optimal designs of video codecs\n\u2022 Exposure to OpenCL based GPU development / CUDA based programming\n\u2022 Aware of Machine learning and Neural Network basics.",
"word_count": 104
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "C"
},
{
"is_primary": true,
"skill_name": "C++"
},
{
"is_primary": true,
"skill_name": "x86"
},
{
"is_primary": true,
"skill_name": "ARM"
},
{
"is_primary": true,
"skill_name": "SSE"
},
{
"is_primary": true,
"skill_name": "AVX"
},
{
"is_primary": true,
"skill_name": "NEON"
},
{
"is_primary": true,
"skill_name": "AV1"
},
{
"is_primary": true,
"skill_name": "H.265"
},
{
"is_primary": false,
"skill_name": "H.264"
},
{
"is_primary": false,
"skill_name": "MPEG-2"
},
{
"is_primary": false,
"skill_name": "VP9"
},
{
"is_primary": true,
"skill_name": "Git"
},
{
"is_primary": false,
"skill_name": "OpenCL"
},
{
"is_primary": false,
"skill_name": "CUDA"
},
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Neural Networks"
}
],
"jd_role": {
"display_name": "Video Codec Engineer",
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"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "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.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"input_skill": "Neural Networks",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "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.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"input_skill": "Neural Networks",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Neural Networks",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "machine_learning_model_concept",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cNeural Networks\u201d is a specific ML model concept; typical JDs won\u2019t confuse it with other distinct skills in the catalog."
},
"context_keywords": {
"context_keywords": [
"TensorFlow",
"Keras",
"PyTorch",
"backpropagation",
"activation functions",
"convolutional layers",
"recurrent networks",
"dropout",
"gradient descent",
"overfitting",
"transfer learning",
"hyperparameters",
"model training",
"loss function",
"neuron"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Neural networks are a core ML concept widely listed in job descriptions for ML/AI roles and underpin mainstream frameworks like PyTorch and TensorFlow, indicating broad market adoption."
},
"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": [],
"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 as a standalone infrastructure concern.",
"exemplar_skills": [
"Neural Networks",
"TensorFlow",
"PyTorch",
"Keras",
"JAX",
"model training",
"inference"
],
"in_scope": "Neural Networks, TensorFlow, PyTorch, Keras, JAX, model definition, backpropagation implementation, training loops, inference APIs",
"name": "Neural Network Frameworks",
"out_of_scope": "Workflow scheduling and orchestration of training jobs, experiment comparison and metric tracking, model governance and safety controls, deployment of models as services",
"overlap_flags": [
{
"reason": "Neural network work often includes training metrics and validation, but that dimension owns run comparison and evaluation workflows.",
"with_dim_id": "experiment-tracking-and-evaluation",
"with_dim_name": null,
"with_role": "ML Engineer, ML Ops Engineer"
},
{
"reason": "Neural networks may be deployed for inference, but serving infrastructure is owned by the deployment dimension.",
"with_dim_id": "llm-serving-deployment",
"with_dim_name": null,
"with_role": "AI 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": [
"scikit-learn",
"tensorflow",
"pytorch",
"vision-language-models",
"multimodal-document-understanding",
"anomaly-detection",
"agentic-systems",
"hybrid-retrieval"
],
"requires": [],
"skill_id": "neural-networks",
"suppress_on_match": []
},
"skill_id": "neural-networks",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.97,
"name": "Neural Networks",
"reasoning": "Neural Networks are a named knowledge unit in machine 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.