LLM / GenAI Engineer

About The Role

The role is for someone who has moved beyond basic prompting and understands what it takes to build production-grade AI systems: robust RAG pipelines, agentic workflows, fine-tuning pipelines, and systematic evaluation frameworks.

The engineering team owns complex pieces of a scalable AI platform, collaborating closely with applied scientists, backend engineers, and stakeholders to deliver high-performance generative AI solutions.

Key Responsibilities

• Design and implement production-ready RAG architectures using LangChain, LlamaIndex, or custom frameworks
• Build and optimize vector database integrations including Pinecone, Weaviate, and pgvector for low-latency semantic search at scale
• Develop systematic LLM evaluation frameworks incorporating benchmark suites, LLM-as-judge pipelines, and regression testing
• Run parameter-efficient fine-tuning pipelines such as LoRA and QLoRA on domain-specific datasets using PyTorch and Hugging Face
• Integrate Large Language Models with external tools and APIs to support complex, multi-step agentic workflows
• Write observable, tested, and well-documented Python code, participating actively in architecture reviews and team deployments
  
  

What We Are Looking For

• 3–6 years of software engineering experience, with a minimum of 2 years specifically focused on building and deploying LLM and GenAI applications in production
• Deep familiarity with LLM orchestration frameworks like LangChain or LlamaIndex and vector database management
• Strong Python development skills including asynchronous programming, REST API design, and cloud infrastructure integration
• Solid understanding of embedding models, tokenization, prompt engineering limits, and foundational transformer architectures
• Bachelor's degree in Computer Science, Artificial Intelligence, or equivalent practical experience
• Bonus: Experience with model quantization, vLLM optimization, TensorRT-LLM, or publishing research in NLP/GenAI domains