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.

• 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 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.

• 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 Zensars 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.