Job Description

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.