Zensar Technologies—Sr. AI / ML Engineer – OpenAI Expert

Job description

Description: 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. Requirements: - 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. 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.