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Niche OpenAI expertise and seniority reduce competition despite metro location and moderate employer brand.
Specialized LLM and OpenAI platform skills transfer across industries but require deep domain expertise.
Explicit 8–12 years, 3+ years OpenAI expertise, and many mandatory tech requirements make filtering strict.
Lead design, development, and deployment of enterprise-grade AI solutions utilizing OpenAI's full platform stack including GPT-4o and Assistants API.
Architect and implement scalable agentic AI systems, RAG pipelines, multi-agent workflows, and enterprise chatbots delivering measurable business impact.
Mentor junior engineers and establish AI engineering best practices within Zensar's AI platform while collaborating with delivery managers and clients to align AI architectures with business needs.
8–12 years of overall AI/ML engineering experience with at least 3 years of hands-on expertise using OpenAI platform and APIs.
Expert-level proficiency with OpenAI APIs (Chat Completions, Assistants API, Function Calling, Structured Outputs, Embeddings, Fine-Tuning).
Strong Python programming skills with experience in async programming, API design, and scalable backend systems.
Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or equivalent practical experience.
Experienced in building production-grade agentic RAG systems, multi-agent orchestration pipelines, and conversational AI solutions at enterprise scale.
Proficient with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, and ML frameworks like PyTorch, TensorFlow, scikit-learn.
Skilled in system design for distributed, fault-tolerant AI architectures, with hands-on knowledge of MLOps tooling and responsible AI principles for production deployment.