LLM / GenAI Engineer

About The Role The role focuses on building, optimizing, and scaling production-grade Generative AI systems, moving beyond basic API wrappers to construct robust RAG pipelines, multi-agent orchestrations, and fine-tuning workflows. The engineer will collaborate closely with product and data platform teams to integrate advanced language models into core enterprise workflows. This position requires deep technical knowledge of LLM mechanics, vector search optimization, and systematic evaluation. The team prioritizes building deterministic, reliable, and low-latency AI features that deliver measurable business value under strict production SLAs. Key Responsibilities Design and optimize advanced Retrieval-Augmented Generation (RAG) pipelines utilizing hybrid search, query rewriting, and reranking modelsDevelop and deploy autonomous agentic workflows and multi-step reasoning systems using LangChain, LangGraph, or custom orchestration frameworksFine-tune open-source models (such as Llama, Mistral) using PEFT techniques like LoRA and QLoRA on domain-specific datasetsBuild and scale low-latency vector database architectures with Pinecone, Qdrant, or pgvector, ensuring efficient indexing and partitioningImplement systematic LLM evaluation and observability frameworks using tools like Arize Phoenix, LangSmith, or Ragas to monitor drift, bias, and accuracyOptimize model inference pipelines for latency and cost using quantization (AWQ, GPTQ) and serving frameworks like vLLM or TGI What We Are Looking For 3-6 years of software engineering experience, with at least 1.5 years of hands-on experience deploying LLMs and generative systems to productionStrong software development skills in Python, including experience with asynchronous programming, FastAPI, and robust unit/integration testingProven experience with vector databases and semantic search optimization techniques at scaleSolid understanding of ML fundamentals, transformer architectures, tokenization, and embedding modelsBS or MS in Computer Science, Data Science, or a related highly quantitative fieldBonus: Experience with model optimization (TensorRT-LLM), custom pre-training, or contributing to open-source GenAI frameworks