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Head of Artificial Intelligence (Agentic AI Platform)

NorthStar HR Consultants

5 - 10 years

Pune

Posted: 05/02/2026

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Job Description

Job Description - Director / Head of Agentic AI Platform & Digital Workers

Job Location - Pune, Maharashtra

Experience - 12+ years

Salary Budget - INR 60 lacs


Our client is hiring a Senior Engineering Leader to build from scratch, own, and scale an enterprise-grade Agentic AI Platform (Agentic AI Fabric) and a portfolio of production Digital Workers (agents) that execute real workflows safely across enterprise systems.


What this leader will own -

1) Build and own the platform (Agentic AI Fabric)

  • Agent runtime/orchestration patterns for multi-step workflows (state, retries, queues/backpressure, approvals/HITL)
  • Tool/action gateway and registry (governed connectors, allow lists, parameter validation, rate limits, rollback)
  • Knowledge/RAG layer (ingestion pipelines, provenance, access controls, grounding and traceability)
  • LLMOps/evaluations (versioned prompts/tools/policies, regression gates, safe rollouts, rollback)
  • Observability and cost governance (telemetry, budgets/alerts, cost-per-transaction, SLOs/runbooks)
  • Multi-cloud-ready architecture patterns (AWS first; extend to Azure later)

2) Build and scale agents as products

  • Deliver a production-grade MVP agent and expand a portfolio of Digital Workers
  • Establish a Next Agent Kit to accelerate subsequent agents (templates, tool patterns, eval harness, rollout checklists)

3) Enterprise agent security baseline (must-have)

This leader must set and enforce controls for:

  • PII/PHI boundary controls and safe logging/data minimization
  • Prompt/tool injection defenses
  • Knowledge base poisoning protections (provenance, approvals/versioning, trust filters)
  • Tool supply-chain integrity (registry governance, version pinning, scanning/SBOM)
  • Privilege escalation controls and step-up approvals / separation of duties


Why this is a senior role

This is not an innovation lab position. It requires:

  • shipping production systems under enterprise constraints,
  • handling security/compliance scrutiny,
  • orchestrating cross-functional teams (engineering, security, SMEs, delivery),
  • and building something reusable and scalable (platform + agents).


Ideal candidate profile (what good looks like)

Must have

  • 1218+ years in engineering/product/platform development
  • 5+ years leading teams and complex delivery programs
  • Track record building enterprise products/platforms (not just projects): multi-tenant SaaS, internal developer platforms, automation/workflow platforms, data/AI platforms
  • Strong cloud architecture experience (AWS and/or Azure), including IAM/security, networking, and observability
  • Hands-on familiarity with LLM applications (RAG + tool calling + evals/monitoring), and the realities of production deployment
  • Experience with


Strongly preferred

Agent frameworks & orchestration

  • Hands-on experience with agent/workflow orchestration frameworks such as LangGraph , CrewAI , AutoGen , and/or similar (e.g., LlamaIndex agents), including multi-step tool calling and human-in-the-loop patterns.

Automation/workflow platforms

  • Experience with workflow automation / integration platforms such as n8n (or comparable low-code workflow engines) to rapidly compose actions and integrate enterprise systems.

Tool integration standards (optional)

  • Familiarity with MCP-style tool servers/tool gateways and agent interoperability concepts (A2A-ready patterns), even if not fully implemented end-to-end.
  • Has built or scaled a platform where customers/users rely on it daily (SLOs, on-call, release governance)
  • Has shipped AI-assisted or autonomous workflow systems that integrate with enterprise apps (ITSM/CRM/ERP)
  • Has worked in regulated contexts (BFSI/healthcare) or delivered audit-grade systems


Nice-to-have

  • Familiarity with agent frameworks, interoperability concepts (A2A/MCP patterns), secure tool execution
  • Experience packaging offerings / enabling GTM narratives for enterprise stakeholders
  • Inference engineering and cost/performance optimization
  • Experience building and operationalizing evaluation frameworks for LLM/agent workflows

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