Senior AI/ML Engineer

About the role

Role summary Implements and tests assigned capabilities across feature suites, including RAG pipeline integration and prompt tuning; delivers GraphRAG-based dynamic query generation, graph-aware retrieval, and knowledge-graph schema evolution support; delivers full orchestration integration for the platform's orchestration component. Key responsibilities Develop feature logic for assigned capabilities within assigned feature suites Design and tune prompts and RAG pipeline integrations Validate sprint-level acceptance criteria against the golden dataset baseline Coordinate with the AI Architect and Backend Engineer on design and integration questions Build GraphRAG-based dynamic query generation to replace template-based SPARQL querying Deliver graph-aware retrieval agent cards Support knowledge-graph schema evolution Coordinate with the KG validation workstream on schema and validation questions Deliver full orchestration integration with the platform's cookbook-based orchestration component Integrate the RAG-layer retrieval pipeline to inform agent selection, routing and response generation Document the upgrade path to fine-tuning (documentation only, not an implementation, in this phase) Expand the golden dataset and semantic grounding Maintain question-and-answer baseline categorization Required skills & experience 6+ years AI engineering experience with strong Python fundamentals Hands-on RAG pipeline and prompt-engineering experience LLM application development experience Comfortable validating work against a capped, governed golden dataset rather than open-ended tuning 3+ years RAG / GraphRAG implementation experience Vector store integration experience Familiarity with SPARQL and graph-aware retrieval Strong Python skills 3+ years RAG implementation and orchestration integration experience Vector store / retrieval pipeline integration experience Comfortable scoping a fine-tuning upgrade path as a design recommendation rather than an implementation 3+ years NLP / semantic engineering experience Ontology mapping and semantic disambiguation experience Experience building or validating golden Q&A datasets for grounding LLM systems Preferred qualifications Experience on a regulated or enterprise AI product Familiarity with LangGraph or comparable agent frameworks Direct experience replacing a template-based SPARQL approach with GraphRAG Experience coordinating with a separate KG validation team Experience with cookbook-style orchestration frameworks Experience writing forward-looking technical design documents (vs. shipping code) when scope calls for it Experience working within a per-sprint expansion cap rather than open-ended dataset growth Pharma/manufacturing terminology disambiguation experience Education B.Tech/MCA in Computer Science, AI/ML, or a related field, or equivalent practical experience.