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 the core AI platform, collaborating closely with applied researchers and backend engineers to deploy scalable, low-latency generative models. Key Responsibilities Design and implement production-grade RAG pipelines using LangChain, LlamaIndex, or custom orchestration architectures Build and optimize vector database integrations such as Pinecone, Weaviate, or pgvector for high-throughput semantic search Develop systematic LLM evaluation frameworks incorporating benchmark suites, LLM-as-judge pipelines, and automated regression testing Execute parameter-efficient fine-tuning (LoRA, QLoRA) on domain-specific datasets using PyTorch and Hugging Face ecosystems Optimize model inference latency and throughput using quantization, vLLM, or TensorRT-LLM in cloud environments Write observable, thoroughly tested code and participate in architectural reviews to maintain high engineering standards What We Are Looking For 3-6 years of software engineering experience, with at least 2 years dedicated to building and deploying LLM applications in production Deep familiarity with LLM orchestration frameworks, prompt engineering best practices, and context window management Strong Python programming skills, with hands-on experience in async programming, REST APIs, and microservices architecture Demonstrated experience with vector databases, embedding models, and semantic search tuning Solid understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization tools like Docker and Kubernetes Bonus: Contributions to open-source AI projects, experience with multimodal models, or a degree in Computer Science, Artificial Intelligence, or a related field Show more Show less