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.
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.
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.