Lead Engineer - LLM/RAG
First seen by Alion on Oct 22, 2026.
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 :
- Experience in fintech, lending, banking, healthcare, or another regulated or data-sensitive industry.
- Familiarity with document understanding models (LayoutLM, Donut, Nougat) and modern OCR tooling (Textract, Tesseract, or equivalents).
- Background in NLP tasks such as named entity recognition, classification, or semantic similarity.
- Experience with LLaMA-family open-weight models and self-hosted serving via Ollama or vLLM.
- Experience contributing to a shared AI platform (prompt registry, evaluation harness, routing layer) used by multiple product teams.
Skills
Generative AI, Python, LLM, RAG, Agentic AI, AWS, LangChain, NLP, LangGraph, LORA