Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production-grade quality, safety, and reliability. Should be familiar with evaluation strategies, responsible AI, explainability.
Responsibilities
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components.
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs.
Architect search & retrieval (BM25 + embeddings) and RAG pipelines drive relevance tuning and evaluation.
Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production-grade quality, safety, and reliability.
Responsibilities
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components.
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs.
Architect search & retrieval (BM25 + embeddings) and RAG pipelines drive relevance tuning and evaluation.
Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream.
Responsibilities
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs
Architect search & retrieval (BM25 + embeddings) and RAG pipelines drive relevance tuning and evaluation
Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production‑grade quality, safety, and reliability.
Responsibilities
Define end‑to‑end architecture for LLM/agent systems and classical ML components
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs
Architect search & retrieval (BM25 + embeddings) and RAG pipelines to drive relevance tuning and evaluation
Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production-grade quality, safety, and reliability.
Responsibilities
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components.
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs.
Architect search & retrieval (BM25 + embeddings) and RAG pipelines drive relevance tuning and evaluation.
Lead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production-grade quality, safety, and reliability.
Responsibilities
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components.
Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs.
Architect search & retrieval (BM25 + embeddings) and RAG pipelines drive relevance tuning and evaluation.