AI Engineer

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

• 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.

• Design, develop, and deploy AI-powered applications using Zensars enterprise AI stack.
• Build and execute proof-of-concepts (POCs) with Large Language Models (LLMs) and modern AI orchestration tools.
• 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.
• Leverage vector stores, embeddings, and Retrieval-Augmented Generation (RAG) architectures.
• Manage source code through CI/CD pipelines and ensure observability of AI applications.
• Contribute to and maintain the internal agent registry and integration standards.
• Perform prompt engineering and fine-tuning for GPT, Gemini, and other LLMs.
• Collaborate with cross-functional teams to align AI solutions with business objectives.
• Document designs, workflows, and best practices for AI development.