All roles 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 Apply for this role 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

→ Robust 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)

→ Strong 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

📌 AI Engineer · Mid–Senior (Anekal)
🏢 Think Right Advisory Services
📍 Anekal