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 Zensars global client portfolio. This is a senior individual contributor role requiring hands-on mastery of OpenAIs 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 Zensars 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 Zensars 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.

• Bachelors or Masters degree in Computer Science, AI, Machine Learning, or equivalent practical experience.

• 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 Zensars ZenseAI.Data platform.

• Collaborate with Zensars 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 OpenAIs usage policies.