Sr. AI / ML Engineer – OpenAI Expert

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. 8–12 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.