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 agile 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.

📌 Senior AI/ML Engineer (Kochi)
🏢 Milestone Technologies
📍 Kochi