What you'll do: Lead AI Engineer in the Platforms and Products will...

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.

What you'll bring:

• A master's or bachelor's degree in Computer Science or related field from a top university
• 4+ years' hands-on experience in Machine Learning (ML) with production LLM systems
• Good fundamentals of machine learning, deep learning and fine tuning models (LLM) including:
  • Understanding of transformer architectures
  • Prompt engineering expertise
  • Embeddings and vector search
• Experienced in backend API design with FastAPI, async patterns, rate limiting
• Experience with vector DB including:
  • Pinecone, Weaviate, or Chroma
  • Embedding storage and similarity search
  • Hybrid search implementations
• Strong programming expertise in Python is must including:
  • Async programming (asyncio, async/await)
  • Type hints and Pydantic
  • SOLID principles and design patterns
• Experience in ML Ops to measure and track model performance including:
  • MLFlow for model tracking
  • Langfuse for LLM observability (strongly preferred)
  • Model versioning and A/B testing
• Experience in working with NLP & computer vision
• Fluency in English
• Client-first mentality
• Intense work ethic
• Collaborative spirit and problem-solving approach

How you'll grow:

• Cross-functional skills development & custom learning pathways
• Milestone training programs aligned to career progression opportunities
• Internal mobility paths that empower growth via s-curves, individual contribution and role expansions

Perks & Benefits:

At ZS, your growth matters. We offer a comprehensive total rewards package that supports your health and well-being, financial future, time away, and professional development. With robust skills-building programs, multiple career progression paths, internal mobility, and a deeply collaborative culture, you'll have the opportunity to do meaningful work, expand your capabilities, and thrive as part of a global community. For details on total rewards in United States, visit ZS US office locations | Where we work | ZS.

Hybrid working model:

We are committed to giving our employees a flexible and connected way of working. A flexible and connected ZS allows us to combine work from home and on-site presence at clients/ZS offices for the majority of our week. The magic of ZS culture and innovation thrives in both planned and spontaneous face-to-face connections.

Travel:

Travel is a requirement at ZS for client facing ZSers; business needs of your project and client are the priority. While some projects may be local, all client-facing ZSers should be prepared to travel as needed. Travel provides opportunities to strengthen client relationships, gain diverse experiences, and enhance professional growth by working in different environments and cultures.