LLM / GenAI Engineer

About The Role

The role focuses on building production-grade generative AI systems, moving beyond basic prompting to architect robust RAG pipelines, agentic workflows, and fine-tuning frameworks.

The engineering team collaborates closely with applied researchers and backend developers to deliver scalable AI capabilities that directly impact enterprise applications.

Key Responsibilities

• Design and deploy advanced RAG pipelines utilizing frameworks such as LangChain and LlamaIndex for large-scale information retrieval
• Optimize vector database integrations including Pinecone, Weaviate, and pgvector for low-latency semantic search
• Implement systematic LLM evaluation frameworks incorporating LLM-as-judge pipelines and automated regression testing
• Execute parameter-efficient fine-tuning workflows like LoRA and QLoRA on domain-specific datasets using PyTorch and Hugging Face
• Build robust backend APIs in Python to serve model inferences reliably in distributed cloud environments
• Monitor production LLM systems for latency, cost, hallucination rates, and performance drift

What We Are Looking For

• 3 to 6 years of software engineering experience, with a minimum of 2 years dedicated to building and deploying LLM-based applications in production
• Proficiency in Python and deep familiarity with ML frameworks such as PyTorch, Transformers, and major orchestration libraries
• Demonstrated experience with vector databases, embedding models, and semantic similarity search at scale
• Solid grasp of prompt engineering strategies, context window management, and model quantization techniques
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related technical field
• Bonus: Experience with distributed training, custom model fine-tuning, or contributions to open-source AI projects