Agentic AI - Manager
- taggd
- 1 Positions
- Noida, Gurugram
- 2 months ago
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Job Description
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Own delivery endtoend: backlog, execution, quality, and timelines for Agentic AI features.
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Be handson (3050% coding): set the technical bar in Python/TypeScript; review PRs; unblock tricky problems.
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Design agentic systems: tooluse orchestration, planning/looping, memory, safety rails, and cost/perf optimization.
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Leverage LLMs smartly: RAG, structured output, function/tool calling, multimodel routing; evaluate build vs. buy.
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Ship production ML/LLM workflows: data pipelines, feature stores, vector indexes, retrievers, model registries.
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MLOps & Observability: automate training/inference CI/CD; monitor quality, drift, toxicity, latency, cost, and usage.
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EVALs & quality: define tasklevel metrics; set up offline/online EVALs (goldens, rubrics, humanintheloop) and guardrails.
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DevOps (Tshaped): own pragmatic infra with the teamGitHub Actions, containers, IaC, basic K8s; keep prod healthy.
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Security & compliance: enforce data privacy, tenancy isolation, PII handling; partner with Security for audits.
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People leadership: recruit, coach, and grow a hightrust team; establish rituals (standups, planning, postmortems).
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Stakeholder management: partner with Product/Design/Recruitment SMEs; translate business goals into roadmaps.
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10+ years in software/ML; 4+ years leading engineers (TL/EM) in highvelocity product teams.
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Built and operated LLMpowered or ML products at scale (userfacing or enterprise workflows).
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Strong coding in Python, Java and TypeScript/Node; solid system design and API fundamentals.
- Exposure to frontend technologies like React, Angular, Flutter
- Experience on SQL databases like Postgres, MariaDB
- Practical MLOps: experiment tracking, model registries, reproducible training, feature/vectors, A/B rollouts.
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LLM tooling: orchestration (LangChain/LlamaIndex/DSPy), vector DBs (pgvector/FAISS/Pinecone/Weaviate), RAG patterns, context engineering
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Observability & EVALs: ML/LLM monitoring, LLM eval frameworks (RAGAS/DeepEval/OpenAI Evals), offline+online testing and human review.
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Comfortable with DevOps: GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP/AWS/Azure).
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Familiar with AI SDLC tools: GitHub Copilot, Cursor, Claude Code, Code Llama/Codexstyle tools; test automation.
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Product mindset: measure outcomes (quality, cost, speed), not just outputs; datadriven decisions.
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HRTech/recruitment domain (ATS/CRM, assessments, interview orchestration).
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Retrieval quality tuning, promptengineering at scale, policy/guardrail systems (OpenAI/Guardrails/NeMo Guardrails).
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Knowledge of multiagent frameworks, graph planners, or workflow engines (Prefect/Temporal).
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Experience with privacy preserving ML, tenancy isolation, regionalization.
Location: Gurgaon
Function: Engineering
Reports to: CTO
Team size: 78 engineers (startup pod)
Why this role
Were building enterprisegrade Agentic AI platform & applications for recruitmentfrom sourcing and screening to interview assistance and offer orchestration. Youll lead a small, highleverage team that ships fast, measures rigorously, and scales responsibly.
What youll do
What youve done (musthaves)
Nice to have
Employement Category:
Employement Type: Full timeIndustry: IT Services & ConsultingRole Category: Embedded / System SoftwareFunctional Area: Not SpecifiedRole/Responsibilies: Agentic AI - ManagerContact Details:
Company: TaggdLocation(s): Noida, Gurugram
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