We are seeking a Senior AI/ML Engineer with deep expertise in OpenAI technologies to design and deliver scalable, enterprise-grade AI solutions for global clients. The role requires hands-on experience in building agentic AI systems, RAG pipelines, and multi-model workflows that drive measurable business outcomes. We are looking for a highly skilled **Sr. AI / ML Engineer with deep OpenAI expertise** to lead the design, development, and deployment of enterprise-grade AI solutions for Zensar's global client portfolio. This is a senior individual contributor role requiring hands-on mastery of **OpenAI's full platform stack** — including GPT-4o, o-series reasoning models, Assistants API, Function Calling, and Fine-Tuning — combined with strong ML engineering fundamentals. The ideal candidate will architect **scalable agentic AI systems, RAG pipelines, and multi-model workflows** that deliver measurable business impact across Zensar's Data Engineering & Analytics service line. ### Responsibilities - Lead end-to-end design and delivery of OpenAI-powered solutions: agentic RAG systems, enterprise chatbots, and AI-driven automation workflows. - Architect and implement multi-agent pipelines using OpenAI Agents SDK, LangGraph, and LangChain, with robust tool-use and memory management. - Leverage the full OpenAI API surface — GPT-4o, o1/o3-mini, Assistants API, Batch API, Structured Outputs, and Vision — for diverse client use cases. - Design and execute fine-tuning strategies for OpenAI models on domain-specific datasets; evaluate using the OpenAI Evals framework. - Build high-performance semantic search and retrieval layers using OpenAI Embeddings integrated with vector databases (Pinecone, pgvector, Azure AI Search). - Develop scalable ML pipelines using Python, PyTorch, and scikit-learn to complement and extend OpenAI model capabilities. - Drive prompt engineering excellence — system prompt design, chain-of-thought reasoning, few-shot learning, and token optimization strategies. - Mentor junior engineers and establish AI engineering best practices, coding standards, and reusable accelerators within Zensar's ZenseAI.Data platform. - Collaborate with Zensar's delivery managers and client stakeholders to translate business requirements into robust AI architectures. - Monitor model performance, cost efficiency, and safety in production; implement guardrails aligned with OpenAI's usage policies. ### Qualifications - 8+ years of overall experience in AI / ML engineering, with at least 3 years of hands-on OpenAI platform expertise. - Expert-level proficiency with OpenAI APIs: Chat Completions, Assistants API, Function Calling, Structured Outputs, Embeddings, and Fine-Tuning. - Deep experience building production-grade agentic RAG systems, conversational AI, and multi-agent orchestration pipelines. - Strong Python engineering skills; experience with async programming, API design, and scalable backend systems. - Hands-on experience with LLM orchestration frameworks: LangChain, LangGraph, LlamaIndex, and OpenAI Agents SDK. - Proficiency in ML frameworks — PyTorch, TensorFlow, scikit-learn — for model development complementary to LLM workflows. - Experience with OpenAI Evals and systematic approaches to model benchmarking, red-teaming, and quality assurance. - Solid understanding of NLP fundamentals: tokenization, embeddings, semantic similarity, named entity recognition, and summarization. - Strong system design skills: ability to architect distributed, fault-tolerant AI systems for enterprise scale. - Excellent communication skills; capable of presenting AI solutions and trade-offs to both technical and executive audiences. - Hands-on experience with Azure OpenAI Service, including managed deployments, content filtering, and private networking. - Familiarity with open-source LLMs (LLaMA 3, Mistral, Phi-3) and ability to benchmark against GPT-4o for cost-performance trade-offs. - Experience with MLOps tooling — MLflow, Weights & Biases, CI/CD for ML — and best practices for production AI observability. - Knowledge of responsible AI principles: bias detection, explainability, hallucination mitigation, and content safety frameworks. - Prior exposure to Zensar's ZenseAI.Data platform, Snowflake, dbt, or Informatica IICS in a data engineering context. - Contributions to open-source AI projects or published technical writing on OpenAI / LLM topics. - Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or equivalent practical experience. ### About the Company At Zensar, we’re *“experience-led everything”*. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: *Together, we shape experiences for better futures*. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is *ONE with Client* - a set of four core values that reflect who we are and how we work: *One Zensar, Nurturing, Empowering, and Client Focus*. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore [Life at Zensar](https://www.zensar.com/careers/) and join us to [Grow. Own. Achieve. Learn.](https://www.youtube.com/embed/i2NZsiQqVnU?autoplay=1&fs=1) to be the best version of yourself. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.