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AI / Applied·Bangalore, IN

- BHIVE Workspace, AKR Tech Park (Kudlu Gate)
- Full-time
- Hybrid·5+ years (3+ years shipping LLMs in production)

AI Engineer Mid–Senior

Build and ship the applied AI layer — the in-product copilot, semantic search, rule-suggestion engine and structured-output features that customers use every day.

LLM FeaturesRAGPrompt EngineeringEval HarnessesPython

Team

AI / Applied

Location

Bangalore, IN

- BHIVE Workspace, AKR Tech Park (Kudlu Gate)

Experience 5+ years (3+ years shipping LLMs in production)

Tech stack

- LangChain
- LlamaIndex
- OpenAI
- Anthropic
- Gemini
- AWS Bedrock
- Pinecone
- pgvector
- Qdrant
- Python (async)
- FastAPI
- BLEU
- ROUGE
- BERTScore
- LoRA
- QLoRA
- PEFT

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What you’ll do

- 01 Implement, evaluate and ship LLM features end-to-end — RAG, tool-use, agents and fine-tunes
- 02 Design and iterate on prompt strategies: chain-of-thought, few-shot, structured outputs, function calling
- 03 Build the in-product AI copilot — answering steward questions, suggesting rules and explaining match decisions
- 04 Develop evaluation harnesses with telemetry, guardrails and offline + online evals
- 05 Integrate and benchmark third-party APIs (OpenAI, Anthropic, Gemini, AWS Bedrock) for cost and latency
- 06 Collaborate with the ML Engineer on embedding strategies, retrieval quality and rerankers




- 07 Work with backend engineers to package AI components as well-defined, observable microservices
- 08 Maintain prompt and model version control with rollback capability for production AI features
- 09 Document system behaviour, failure modes and known limitations for every shipped AI feature

What we’re looking for
- → 5+ years software engineering experience; 3+ years working directly with LLMs in production
- → Strong Python — async APIs, data pipelines and clean, testable code
- → Hands-on with LangChain, LlamaIndex or equivalent orchestration frameworks
- → Experience with OpenAI / Anthropic / Gemini APIs including function calling and structured outputs
- → Working knowledge of embedding models and vector databases (Pinecone, pgvector, Qdrant)
- → Solid NLP fundamentals: tokenization, NER, relation extraction and summarisation
- → Solid grasp of evaluation methodology — BLEU/ROUGE/BERTScore plus task-specific evals
- → Experience shipping AI features end-to-end from prototype to production

Nice to have
- - Experience with B2B data platforms, MDM, entity resolution or recommendation systems

- - Exposure to fine-tuning LLMs (LoRA / QLoRA, PEFT, instruction tuning)

- - Familiarity with Salesforce or Databricks ecosystems

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