Role overview
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ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.
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What you'll work on
We are seeking a highly motivated Applied AI Engineer with a strong foundation in Machine Learning and a deep interest in Large Language Models (LLMs) and Generative AI. This role focuses on building, optimizing, and evaluating production-grade LLM systems, including Retrieval-Augmented Generation (RAG), fine-tuning workflows, and scalable inference pipelines.
- Design and implement LLM-powered applications using state-of-the-art transformer models.
- Build and optimize RAG pipelines using embeddings, chunking strategies, and vector search.
- Experiment with prompt engineering, structured outputs (JSON schemas/function calling), and tool-augmented LLMs (agents/workflows).
- Fine-tune models using techniques such as LoRA, PEFT, and instruction tuning.
- Develop and evaluate embedding models for similarity search and semantic retrieval.
- Conduct LLM evaluation using automated and human-in-the-loop techniques (offline + online).
- Optimize inference workflows for latency, GPU utilization, and cost efficiency (quantization, batching, caching).
- Build and maintain REST API Services (FastAPI etc.) to deploy LLM/RAG endpoints, integrate with product systems, and support scalable inference.
- Contribute to integration of AI systems into production software environments (CI/CD, monitoring, reliability).
- Research and prototype cutting-edge approaches in Generative AI and share learnings with the team.