LLM / GenAI Engineer

The role focuses on building and optimizing production-grade Generative AI systems, moving beyond basic API wrappers to design robust Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and fine-tuning pipelines.

The engineer will work closely with data platform teams and product engineers to integrate large language models into enterprise-scale features where throughput, latency, cost, and accuracy are critical constraints.

Key Responsibilities

• Design and optimize RAG pipelines utilizing advanced chunking strategies, reranking models, and hybrid search methods.
• Build and maintain high-performance vector database integrations using tools like Qdrant, Pinecone, or pgvector at scale.
• Implement systematic LLM evaluation and monitoring frameworks to detect hallucinations, measure response quality, and track latency.
• Fine-tune open-source models (such as Llama, Mistral) using parameter-efficient methods like LoRA and QLoRA for domain-specific tasks.
• Develop and deploy robust orchestration layers and agentic workflows using LangChain, LangGraph, or custom lightweight frameworks.
• Collaborate with MLOps to containerize, deploy, and monitor LLM inference endpoints in cloud environments using vLLM or Triton Inference Server.

• 3–6 years of software engineering experience, with at least 1.5 years dedicated to building and deploying LLM-based applications in production.
• Strong software engineering fundamentals in Python, including async programming, API development (FastAPI), and writing comprehensive unit and integration tests.
• Hands-on experience with vector databases and semantic search optimization.
• Familiarity with model optimization techniques such as quantization, caching strategies, and structured output generation (e.g., Outlines, Instructor).
• Bachelor's degree in Computer Science, engineering, or a related quantitative field, or equivalent practical experience.
• Bonus: Experience with direct fine-tuning datasets preparation, hands-on Kubernetes usage, or contributions to open-source GenAI frameworks.