Lead Engineer - LLM/RAG

Urgent opening with R systems.Required Qualifications :

Job location : Bangalore (Mandatory).

Work from Office : Yes.

Looking for Immediate joiner only.

Exp level : 6 - 9 Yrs.

Core Experience :

• 4+ years of software engineering experience, with 2+ years building and shipping LLM-powered applications in production.

• Strong Python skills - production-quality code, clean architecture, type-safe data modeling (Pydantic or equivalent), and testable design.

• Hands-on production experience with at least one major LLM provider - AWS Bedrock, Anthropic Claude, OpenAI GPT, Google Gemini, or equivalent - including tool/function calling and structured output.

• Demonstrated experience building RAG systems end to end - chunking, embeddings, vector databases (Qdrant, Pinecone, Weaviate, OpenSearch, or pgvector), retrieval, and re-ranking.

• Hands-on experience building agentic workflows - LLM agents that call tools, reason across multiple steps, and autonomously complete multi-stage tasks with appropriate safeguards.

• Experience with fine-tuning and adaptation workflows - LoRA, QLoRA, instruction tuning, or preference tuning - and with evaluating model outputs rigorously rather than by demo.

Engineering & Platform Skills :

• Solid LLM tooling fluency - LangChain or LangGraph, LlamaIndex, DSPy, Hugging Face, and the judgment to pick the right tool rather than adopt reflexively.

• Prompt engineering craft - system prompts, few-shot examples, chain-of-thought, structured output schemas, and tool/function calling.

• Comfort working with unstructured data - extracting, classifying, and generating structured outputs from text-heavy inputs, including documents and forms.

• Cloud and deployment fluency - AWS preferred (including Bedrock), plus containerization (Docker), and familiarity with self-hosted LLM serving (vLLM, TGI, Ollama, or similar).

• Evaluation discipline - ability to write tests for non-deterministic systems, build golden sets, and reason about output quality at scale.

• Strong debugging instincts for LLM-specific failure modes - hallucinations, retrieval gaps, prompt drift, and latency spikes.

Security & Regulated-Industry Awareness :

• Working awareness of LLM security concerns - prompt injection, data exfiltration, output filtering, and PII handling.

• Respect for data sensitivity and an instinct to keep borrower PII and financial data inside Lendistry's trust boundary by default.

Preferred Qualifications :