AI Engineer

Role overview

AI Engineer at Zensar

The role involves end‑to‑end development, from model integration and prompt engineering to CI/CD deployment and observability, while collaborating across functions to ensure alignment with strategic objectives.

Responsibilities

• Design, develop, and deploy AI‑powered applications using Zensar's enterprise AI stack.
• Create intelligent agents and multi‑agent systems using LangGraph and agent builder platforms.
• Implement Model Context Protocol (MCP) integrations, MCP Tools, and agent‑to‑agent (A2A) communication patterns.
• Manage source code through CI/CD pipelines and ensure observability of AI applications.
• Contribute to and maintain the internal agent registry and integration standards.
• Collaborate with cross‑functional teams to align AI solutions with business objectives.

Employment details

• Location: Pune [Maharashtra] [India]

About the employer

• Zensar Technologies Limited is seeking a forward‑thinking AI Engineer to join our innovative team.

Additional role details

• You will work with our enterprise AI stack to build cutting‑edge applications, proof‑of‑concepts, and intelligent multi‑agent systems that drive business value.
• AI Engineer to design, develop, and deploy enterprise AI solutions, including LLM‑driven agents and RAG systems, at Zensar Technologies Limited.
• Build and execute proof‑of‑concepts (POCs) with Large Language Models (LLMs) and modern AI orchestration tools.
• Leverage vector stores, embeddings, and Retrieval‑Augmented Generation (RAG) architectures.
• Perform prompt engineering and fine‑tuning for GPT, Gemini, and other LLMs.
• Document designs, workflows, and best practices for AI development.
• Hands‑on experience integrating LLMs (e.g., GPT, Gemini) and prompt engineering.
• Proficiency with LangGraph and LangSmith for building AI workflows and agents.
• Experience with agent builder platforms, multi‑agent systems, and orchestration.
• Familiarity with Model Context Protocol (MCP), MCP Tools, and A2A communication.
• Strong programming skills in Python and familiarity with AI/ML libraries (e.g., PyTorch, TensorFlow).
• Knowledge of vector databases, embeddings, and RAG architectures.
• Experience with CI/CD tools (e.g., Jenkins, GitHub Actions) and monitoring/observability solutions.
• Bachelor’s or higher degree in Computer Science, AI, Machine Learning, or related field.
• Excellent problem‑solving abilities and a collaborative mindset.
• Ability to work in an agile environment and manage multiple priorities.