Sr.AI/ML Engineer at G2 Technology… · Coimbatore

Senior AI/ML Engineer — Enterprise AI Framework

Experience: 4–8 Years

Role Type: Full-time

Primary Area: Applied AI / Generative AI / RAG Engineering

Project: Enterprise AI Framework (EAF)

Role Summary

Senior AI/ML Engineer to design, build, evaluate and mature the intelligence layer of our Enterprise AI Framework.

The role focuses primarily on production-grade Retrieval-Augmented Generation (RAG), retrieval quality, embeddings, reranking, context engineering, LLM integration, guardrails and AI evaluation.

This is not a prompt-engineering-only role and it is not primarily a model-training/research role.

The engineer must understand how to build reliable AI systems in which responses are grounded in governed enterprise evidence and important business decisions remain deterministic, auditable and human-controlled.

Key Responsibilities

• Design and improve production RAG/retrieval pipelines.
• Develop semantic, keyword and hybrid retrieval strategies.
• Tune chunking, embeddings, metadata filters, top-K selection and reranking.
• Diagnose retrieval failures and improve Recall@K, MRR, precision and related quality measures.
• Design evidence-aware context assembly and context prioritization.
• Prevent irrelevant/context-overloaded prompts.
• Develop and maintain LLM gateway/model abstraction components.
• Integrate commercial or open-source LLMs through controlled interfaces.
• Implement model timeout, retry, fallback, cost and token controls.
• Develop versioned system prompts and structured output contracts.
• Build AI guardrails for grounding, citations, unsupported claims, PII and policy constraints.
• Work with deterministic business logic and scoring engines without transferring decision authority to an LLM.
• Design evaluation datasets/golden sets.
• Build automated retrieval, prompt and model regression tests.
• Establish trustworthy offline and online AI-quality measurement.
• Investigate hallucination, retrieval drift and context-quality failures.
• Evaluate and integrate rerankers/cross-encoders where justified.
• Work with local and hosted embedding models.
• Contribute to graph-assisted retrieval/entity-aware context where quality evidence justifies it.
• Support future proactive/agentic workflows while maintaining bounded actions and human accountability.
• Instrument AI components for quality, latency, tokens, cost and guardrail outcomes.
• Work with Data Engineering to ensure ingestion/indexing quality and with Backend Engineering for APIs/orchestration/security.
• Conduct architecture/design reviews and mentor less-experienced AI engineers.

Must-Have SkillsApplied AI / ML Engineering

• 4–8 years overall engineering/AI/ML experience with substantial hands-on software development.
• Strong Python programming.
• Solid machine-learning fundamentals.
• Hands-on production experience with LLM/Generative AI applications.
• Strong understanding of Retrieval-Augmented Generation.
• Experience with embeddings and semantic search.
• Experience with vector databases/search platforms.
• Strong understanding of document chunking and retrieval design.
• Experience integrating LLM APIs.
• Prompt/context engineering.
• Automated evaluation/testing experience.
• Ability to interpret retrieval and ranking metrics.
• Strong debugging and system-design ability.

RAG / Search Depth

Candidate should understand:

• semantic retrieval;
• keyword retrieval;
• hybrid search;
• metadata filtering;
• embeddings;
• top-K;
• reranking;
• cross-encoders;
• context assembly;
• grounding;
• hallucination;
• citations;
• retrieval evaluation.

Equivalent technology experience is acceptable.

Strongly Preferred

• Qdrant, pgvector, Pinecone, Weaviate, Milvus or similar.
• BM25/hybrid retrieval.
• Cross-encoder reranking.
• sentence-transformers.
• Claude, OpenAI or equivalent LLM APIs.
• RAG evaluation frameworks.
• Promptfoo or similar regression tooling.
• RAGAS or equivalent evaluation methodology.
• PII/guardrail implementations.
• Structured LLM outputs / JSON Schema.
• OpenTelemetry.
• Production AI observability.
• Knowledge graph / GraphRAG concepts.
• Entity-aware retrieval.

Nice to Have

• Neo4j or graph databases.
• LangChain, LlamaIndex, LangGraph or equivalent.
• Model gateways such as LiteLLM.
• MLflow / Weights & Biases.
• AWS AI infrastructure.
• Prompt-injection/adversarial testing.
• AI security and governance.
• Agentic workflow design.
• Fine-tuning experience.

Fine-tuning experience is useful but not a core requirement for the current EAF role

Pay: ₹100,000.00 - ₹200,000.00 per month

Benefits:

• Life insurance
• Provident Fund

Work Location: In person