Responsibilities
- Define and own the enterprise AI architecture, spanning LLMs, SLMs, fine-tuned models, and retrieval-augmented systems
- Establish model selection frameworks
- Design and govern AI infrastructure: vector databases, embedding pipelines, model registries, and inference optimization
- Lead the adoption and governance of Model Context Protocols (MCPs)
- Architect multi‑agent systems capable of reasoning, planning, and executing across complex business processes
- Design agent orchestration patterns
- Lead end‑to‑end business process automation using AI
- Collaborate with process owners to identify and prioritize automation opportunities with measurable ROI
- Develop and maintain the enterprise AI roadmap
- Build and enforce AI governance frameworks
- Act as the internal AI thought leader
- Recruit, develop, and lead a high‑performing AI team
- Build a center‑of‑excellence model that distributes AI capability and fluency across business units
Requirements
- 10+ years in software engineering, data, or applied AI with at least 3 years in senior AI/ML leadership role
- Deep hands‑on expertise with large language models (GPT-4 class, Claude, Llama, Mistral) and small language models in production
- Proven track record architecting and deploying agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent
- Strong command of Model Context Protocols (MCPs) and tool‑use patterns for connecting agents to enterprise systems
- Experience leading end‑to‑end AI‑powered business process automation — not just prototypes, but production‑grade systems with SLAs
- Proficiency in Python and familiarity with cloud AI ecosystems (AWS Bedrock, Azure OpenAI, GCP Vertex AI)
- Demonstrated ability to translate ambiguous business problems into structured, solvable AI architectures
- Must have used Claude Code, Codex or Github Copilot in the prior roles
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