Pipeline run
fa0f1898-b512-4d91-97e6-b14f216df9d3
Pipeline LLM cost (USD)
API 1: $0.0049
API 2: $0.0000
API 3: $0.0000
Total: $0.0049
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD
Nature of work
—
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
✓ Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
embeddings, agentic AI, agentic, AI
Evidence — skills matched in JD (4)
Python
Pachyderm
Prefect
APIs
Skill cluster (0 dimension groups, role-scoped)
Show KRA description ↓
Manomay is building a large-scale AI Consulting Practice focused on embedding AI into real business operations — pragmatically and responsibly. To kick-start this journey, we are looking for a highly hands-on AI Systems Engineer who will work closely with our internal data engineering team and business architects to build end-to-end AI practice agents. This is a builder-first role. The focus is on designing, coding, integrating, and delivering real AI systems that are used daily inside Manomay and later showcased to clients as reference implementations. Build AI agents for how we work — and for whatever our clients need across the insurance value chain. This is not a “Head” role. This is a hands-on leadership role where credibility comes from building and delivering.
1. Internal Efficiency (Manomay) Build agents that improve how we deliver:
• Strategy (TOM, process thinking)
• Business analysis and documentation
• Data migration preparation and validation
• QA / testing acceleration
• Reporting and insights
Goal:
Reduce effort. Improve quality. Increase speed.
2. Client-led Agent Development (Insurance Value Chain)
• Work with teams and clients to identify:
o Real problems
o Bottlenecks
o Decision gaps
• Build agents based on the need, not predefined categories:
o Could be underwriting
o Could be claims
o Could be data
o Could be operations
o Could be something else entirely
• Design systems where:
o AI handles standard flows
o Humans handle exceptions
Goal:
Create AI that fits the problem, not the other way around.
• 7–12+ years in Engineering / AI / Data
• Experience in Insurance + Agentic AI is a strong plus
• Strong in:
o Python
o APIs, system design, integrations
o Data pipelines and workflows
• Experience building production-grade systems
• Experience in:
o Agentic AI / workflow-driven systems
o Multi-step decision systems
o Combining rules + AI + data
• Ability to:
o Understand real-world problems
o Lead through hands-on building
o Operate across internal + external contexts
o Build solutions without over-structuring upfront
• The candidate should be interested and experienced in providing guidance to junior members of the team.
• You don’t start with: “What can AI do?”
• You start with: “What is the real problem here?”
• You are comfortable with:
o Ambiguity
o Changing requirements
o Evolving solutions
• Agents built and used:
o Internally (efficiency)
o Externally (client value)
• Tangible impact:
o Reduced manual effort
o Faster turnaround
o Better decision-making
Status:
jd_deconstruction_v4_done
Created: 2026-09-19T16:41:29.988200Z
Updated: 2026-09-24T06:29:07.050012Z
Run duration: 34069 ms
Flow
v4 catalog deconstruction
skill_library_v4
1 LLM mention extraction + deterministic catalog sweep
2 Six-layer resolution cascade (exact → alias → fuzzy → embedding → judge → AI-prediction lane)
3 Table-lookup layering: dim_members → role_dim_map tiers, served verbatim
Role
Chosen role & resolution
ML Engineer
in_db alias family · ml-engineerslug: ml-engineer · role_id: 4f5b94dc-3efd-55f4-bafc-2858469ea293 · catalog: skill_library_v4 (tiers served verbatim from role_dim_map)
Manomay Insurtech is seeking a Lead AI Engineer with 6 to 10 years of experience to join their AI Consulting Practice in Hyderabad. The role focuses on building end-to-end AI systems specifically for the insurance value chain, utilizing technologies such as Python, APIs, and data pipelines. Distinctive requirements include experience in Agentic AI and the ability to create solutions based on real-world problems rather than predefined use cases. The position emphasizes hands-on leadership and collaboration with internal teams and clients to enhance operational efficiency and deliver impactful AI solutions.
Job description
MANOMAY INNSURTECH Designation: Lead AI Engineer – Agentic AI (Insurance) Experience: 6 to 10 Years Location: Hyderabad Reporting to: Head of Data Role Summary Manomay is building a large-scale AI Consulting Practice focused on embedding AI into real business operations — pragmatically and responsibly. To kick-start this journey, we are looking for a highly hands-on AI Systems Engineer who will work closely with our internal data engineering team and business architects to build end-to- end AI practice agents. This is a builder-first role. The focus is on designing, coding, integrating, and delivering real AI systems that are used daily inside Manomay and later showcased to clients as reference implementations. Build AI agents for how we work — and for whatever our clients need across the insurance value chain This is not a “Head” role. This is a hands-on leadership role where credibility comes from building and delivering. Why this role At Manomay Insurtech, our focus is simple: 1. Build agents for internal efficiencies across our services 2. Build agents based on real client needs — across the insurance value chain We don’t believe in pre-defined AI use cases. We believe in building what is needed, where it matters. What you will do 1. Internal Efficiency (Manomay) Build agents that improve how we deliver: • Strategy (TOM, process thinking) • Business analysis and documentation • Data migration preparation and validation • QA / testing acceleration • Reporting and insights Goal: Reduce effort. Improve quality. Increase speed. 2. Client-led Agent Development (Insurance Value Chain) • Work with teams and clients to identify: o Real problems o Bottlenecks o Decision gaps • Build agents based on the need, not predefined categories: o Could be underwriting o Could be claims o Could be data o Could be operations o Could be something else entirely • Design systems where: o AI handles standard flows o Humans handle exceptions Goal: Create AI that fits the problem, not the other way around. What we are looking for • 7–12+ years in Engineering / AI / Data • Experience in Insurance + Agentic AI is a strong plus • Strong in: o Python o APIs, system design, integrations o Data pipelines and workflows • Experience building production-grade systems • Experience in: o Agentic AI / workflow-driven systems o Multi-step decision systems o Combining rules + AI + data • Ability to: o Understand real-world problems o Lead through hands-on building o Operate across internal + external contexts o Build solutions without over-structuring upfront • The candidate should be interested and experienced in providing guidance to junior members of the team. How you think • You don’t start with: “What can AI do?” • You start with: “What is the real problem here?” • You are comfortable with: o Ambiguity o Changing requirements o Evolving solutions What success looks like • Agents built and used: o Internally (efficiency) o Externally (client value) • Tangible impact: o Reduced manual effort o Faster turnaround o Better decision-making Why join Manomay • Work on real problems, not predefined AI use cases • Build systems that are used in day-to-day operations • Be part of shaping AI-led service delivery in insurance Note: We are not looking for someone to apply AI. We are looking for someone who can build what is needed — when it is needed. Join Manomay in Shaping the Future of Insurance Technology: Our influence extends across the Caribbean Islands and the USA, where over the last 14 years, we have built a robust, highly satisfied, and dedicated client base by delivering exceptional and innovative services and solutions. Our deep industry knowledge and commitment to excellence have earned us the trust of leading insurance companies, positioning us as a pivotal player in the Insurance IT & Insurtech space. • Who will be at the forefront of technological advancements in the insurance industry, and being part of a team that is constantly pushing the boundaries of what's possible with technology. • Whose contributions will directly influence the transformation of the insurance industry, driving efficiency, enhancing customer experience, and fostering innovation. • Who will have the opportunity to work on projects that make a tangible difference in diverse markets. • Who believes in the power of collaboration and team-oriented approach ensuring that every voice is heard and that we achieve our goals together. • Who are committed to the continuous development of our employees, have access to ongoing learning opportunities, mentorship, and working with cutting-edge technologies. Our value system is deeply ingrained in upholding the highest standards of integrity in all our actions, developing relationships that make a positive difference in our clients' lives, providing unsurpassed service and outstanding products that, together, deliver premium value, and constantly seeking new and better ways to serve our clients and improve the insurance industry. Our people are our strength, and they work with entrepreneurial zeal and contribute extensively to strategic projects that align with our vision and goals. As an exceptional performer you will team up with talented professionals fostering a culture of innovation and excellence and help define the future of Insurance IT and Insurtech. If you are passionate about modern IT technologies and are excited about the ever dynamic insurance industry, we invite you to be part of our exciting journey. Together, we can reshape the future of insurance technology and deliver unparalleled value to our clients. How to Apply: Please submit your resume via email detailing your relevant qualifications and experience and why you are the ideal candidate for this role, along with references, to anila.talurpudikinad@manomay.biz
Skills from this JD
L0–L3 tiers come verbatim from the role's role_dim_map; violet AI GENERATED chips are AI-prediction-lane skills pending review in the queue.
L0 · Anchor
1 skill(s)
Python
Programming Languages
Python is the working surface for the entire role; SQL is how training data is assembled.
L2 · Environment
3 skill(s)
Pachyderm
Workflow Orchestration
Training and retraining run on a schedule someone else usually owns.
Prefect
Workflow Orchestration
Training and retraining run on a schedule someone else usually owns.
APIs
AI GENERATED
API Design & Protocols
TaBuddy AI prediction — not in the skill library yet; placed under API Design & Protocols pending admin review.
Secondary
nice-to-have · not a drop-order layer
Agentic AI
AI GENERATED
not_in_catalog
This skill is in Secondary as the JD says "Experience in Insurance + Agentic AI is a strong plus" - it isn't in this role's skill catalog yet, so it's tracked for review.
Workflow-Driven Systems
AI GENERATED
not_in_catalog
This skill is in Secondary as the JD says "Agentic AI / workflow-driven systems" - it isn't in this role's skill catalog yet, so it's tracked for review.
Consider adding
canon exemplars the JD missed
Deep Learning — Applied ML Modeling & Methods (L0)
PyTorch — ML Frameworks & Model Training (L0)
SQL — Programming Languages (L0)
Apache Spark — Data Processing & Pipeline Frameworks (L1)
MLflow — Experiment Tracking & Model Lifecycle (L1)
Feast — Feature Engineering & Feature Stores (L1)
Unmapped
not resolved, not classified — quarantined for review
Multi-step decision systemssystem design
Library artifacts (this run)
No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleLead AI Engineer – Agentic AI (Insurance)
CompanyMANOMAY INNSURTECH
Experience7–12+ years in Engineering / AI / Data
DomainInsurance
Location
Hyderabad, India
(null)
JD type
pass
Show raw JSON
{
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},
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{
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"source_marker": {
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},
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"word_count": 36
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}
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"urls": []
}
API 1 — extract-from-jd click to toggle
{
"catalog": "v4",
"consider_adding": [
{
"dimension": "Applied ML Modeling \u0026 Methods",
"reason": "In the role canon\u0027s Applied ML Modeling \u0026 Methods - adding it narrows the candidate pool.",
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"tier": "L0"
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{
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{
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},
{
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},
{
"dimension": "Feature Engineering \u0026 Feature Stores",
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"final_skills": [
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"kras": [
{
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{
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{
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{
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{
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},
{
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"source": "graph",
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},
{
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},
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{
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{
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},
{
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"source": "graph",
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},
{
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},
{
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"salience": 0.35,
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},
{
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"role_phrasing": "Instruments model services so prediction behaviour is visible alongside system metrics.",
"role_slug": "ml-engineer",
"salience": 0.35,
"source": "graph",
"weight": 0.35
}
],
"layer_conflicts": [],
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Join Manomay in Shaping the",
"last_5_words": "in the Insurance IT \u0026 Insurtech space."
},
"text": "Join Manomay in Shaping the Future of Insurance Technology: Our influence extends across the Caribbean Islands and the USA, where over the last 14 years, we have built a robust, highly satisfied, and dedicated client base by delivering exceptional and innovative services and solutions. Our deep industry knowledge and commitment to excellence have earned us the trust of leading insurance companies, positioning us as a pivotal player in the Insurance IT \u0026 Insurtech space.",
"word_count": 64
},
"ai_kras": [],
"certifications": [],
"company_name": "MANOMAY INNSURTECH",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Insurtech",
"Insurance Technology"
],
"domain": "Insurance"
},
"secondary": null
},
"education": [],
"experience": {
"max": 12,
"min": 7,
"raw": "7\u201312+ years in Engineering / AI / Data"
},
"job_locations": [
{
"aliases": [],
"city": "Hyderabad",
"country": "India",
"state": null,
"work_mode": "null"
}
],
"role": "Lead AI Engineer \u2013 Agentic AI (Insurance)",
"role_aliases": [
{
"name": "AI Engineer",
"reasoning": "generalized form of the picked role",
"relation": "synonym"
},
{
"name": "AI Systems Engineer",
"reasoning": "role involves building AI systems",
"relation": "synonym"
},
{
"name": "Machine Learning Engineer",
"reasoning": "role involves AI and engineering aspects",
"relation": "adjacent"
}
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Role Summary",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Manomay is building a large-scale",
"last_5_words": "building and delivering."
},
"text": "Manomay is building a large-scale AI Consulting Practice focused on embedding AI into real business operations \u2014 pragmatically and responsibly. To kick-start this journey, we are looking for a highly hands-on AI Systems Engineer who will work closely with our internal data engineering team and business architects to build end-to-end AI practice agents. This is a builder-first role. The focus is on designing, coding, integrating, and delivering real AI systems that are used daily inside Manomay and later showcased to clients as reference implementations. Build AI agents for how we work \u2014 and for whatever our clients need across the insurance value chain. This is not a \u201cHead\u201d role. This is a hands-on leadership role where credibility comes from building and delivering.",
"word_count": 114
},
{
"bullet_count": 10,
"heading": "What you will do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Internal Efficiency (Manomay) Build",
"last_5_words": "the problem, not the other"
},
"text": "1. Internal Efficiency (Manomay) Build agents that improve how we deliver:\n\u2022 Strategy (TOM, process thinking)\n\u2022 Business analysis and documentation\n\u2022 Data migration preparation and validation\n\u2022 QA / testing acceleration\n\u2022 Reporting and insights\n\nGoal:\nReduce effort. Improve quality. Increase speed.\n\n2. Client-led Agent Development (Insurance Value Chain)\n\u2022 Work with teams and clients to identify:\n o Real problems\n o Bottlenecks\n o Decision gaps\n\u2022 Build agents based on the need, not predefined categories:\n o Could be underwriting\n o Could be claims\n o Could be data\n o Could be operations\n o Could be something else entirely\n\u2022 Design systems where:\n o AI handles standard flows\n o Humans handle exceptions\n\nGoal:\nCreate AI that fits the problem, not the other way around.",
"word_count": 205
},
{
"bullet_count": 8,
"heading": "What we are looking for",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 7\u201312+ years in Engineering",
"last_5_words": "guidance to junior members of"
},
"text": "\u2022 7\u201312+ years in Engineering / AI / Data\n\u2022 Experience in Insurance + Agentic AI is a strong plus\n\u2022 Strong in:\n o Python\n o APIs, system design, integrations\n o Data pipelines and workflows\n\u2022 Experience building production-grade systems\n\u2022 Experience in:\n o Agentic AI / workflow-driven systems\n o Multi-step decision systems\n o Combining rules + AI + data\n\u2022 Ability to:\n o Understand real-world problems\n o Lead through hands-on building\n o Operate across internal + external contexts\n o Build solutions without over-structuring upfront\n\u2022 The candidate should be interested and experienced in providing guidance to junior members of the team.",
"word_count": 139
},
{
"bullet_count": 3,
"heading": "How you think",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 You don\u2019t start with:",
"last_5_words": "requirements\n o Evolving solutions"
},
"text": "\u2022 You don\u2019t start with: \u201cWhat can AI do?\u201d\n\u2022 You start with: \u201cWhat is the real problem here?\u201d\n\u2022 You are comfortable with:\n o Ambiguity\n o Changing requirements\n o Evolving solutions",
"word_count": 36
},
{
"bullet_count": 6,
"heading": "What success looks like",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Agents built and used:",
"last_5_words": "faster turnaround\n o Better decision-making"
},
"text": "\u2022 Agents built and used:\n o Internally (efficiency)\n o Externally (client value)\n\u2022 Tangible impact:\n o Reduced manual effort\n o Faster turnaround\n o Better decision-making",
"word_count": 34
}
],
"urls": []
},
"pipeline": "v4",
"rejected": false,
"rejection_code": null,
"rejection_reason": null,
"role": {
"canonical_name": "ML Engineer",
"election_confidence": 0.0,
"family": "ml-engineer",
"match_method": "alias",
"resolution": "in_db",
"role_id": "4f5b94dc-3efd-55f4-bafc-2858469ea293",
"similarity": null,
"slug": "ml-engineer"
},
"role_coverage": [
{
"is_jd_role": true,
"name": "ML Engineer",
"pct": 0.22,
"role": "ml-engineer",
"share": 100.0
}
],
"run_id": "jdv4-9c1bc21abc78",
"secondary_meta": [
{
"audit_reasoning": "This skill is in Secondary as the JD says \"Experience in Insurance + Agentic AI is a strong plus\" - it isn\u0027t in this role\u0027s skill catalog yet, so it\u0027s tracked for review.",
"jd_quote": "Experience in Insurance + Agentic AI is a strong plus",
"origin": "ai_predicted",
"provenance": "listed",
"reason_code": "not_in_catalog",
"skill": "Agentic AI"
},
{
"audit_reasoning": "This skill is in Secondary as the JD says \"Agentic AI / workflow-driven systems\" - it isn\u0027t in this role\u0027s skill catalog yet, so it\u0027s tracked for review.",
"jd_quote": "Agentic AI / workflow-driven systems",
"origin": "ai_predicted",
"provenance": "listed",
"reason_code": "not_in_catalog",
"skill": "Workflow-Driven Systems"
}
],
"secondary_skills": [
"Agentic AI",
"Workflow-Driven Systems"
],
"skill_clusters": [],
"skill_layers": [
{
"label": "Anchor",
"layer": "L0",
"skills": [
{
"dimension": {
"display_name": "Programming Languages",
"slug": "programming-languages"
},
"name": "Python",
"origin": "catalog",
"rationale": "Python is the working surface for the entire role; SQL is how training data is assembled."
}
]
},
{
"label": "Environment",
"layer": "L2",
"skills": [
{
"dimension": {
"display_name": "Workflow Orchestration",
"slug": "workflow-orchestration"
},
"name": "Pachyderm",
"origin": "catalog",
"rationale": "Training and retraining run on a schedule someone else usually owns."
},
{
"dimension": {
"display_name": "Workflow Orchestration",
"slug": "workflow-orchestration"
},
"name": "Prefect",
"origin": "catalog",
"rationale": "Training and retraining run on a schedule someone else usually owns."
},
{
"dimension": {
"display_name": "API Design \u0026 Protocols",
"slug": "api-design-protocols"
},
"name": "APIs",
"origin": "ai_predicted",
"rationale": "TaBuddy AI prediction \u2014 not in the skill library yet; placed under API Design \u0026 Protocols pending admin review."
}
]
}
],
"unmapped_skills": [
"Multi-step decision systems",
"system design"
]
}
API 2 — extract-details
{}
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.
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