Engineer Agentic

Phenom; Dadri; On-site; INR 1,250,000 - 2,100,000; Full time

Job summary

Phenom is seeking an AI Engineer to design, develop, deploy, and maintain AI agents that deliver business value. You will leverage Generative AI, LLMs and Microsoft AI Foundry to build scalable, secure, production-ready AI solutions.

The role requires hands-on experience with Azure AI Foundry, Azure OpenAI, Python, and related tooling, and involves collaboration with data engineering teams to ensure data quality and governance in AI deployments.

Qualifications

• B.E. / B.Tech (Computer Science / Data Science or related field)
• 4+ years in Data Science & Analytics with 2+ years in agent development
• Experience with Azure AI Foundry, Azure OpenAI, and LLM-related technologies
• Understanding of data governance, security and compliance in AI deployments

Responsibilities

• Design, develop, deploy and maintain AI agents using Azure AI Foundry and related tools
• Build RAG solutions by integrating enterprise data sources and vector stores
• Ensure production readiness with secure, governable and compliant AI solutions

Skills

Agent development

Python

AI/ML

REST APIs

Education

B.E. / B.Tech in CS / Data Science

Tools

Azure AI Foundry

Azure OpenAI

LangChain / LangGraph / CrewAI

MLOps / CI/CD

Job description

Job Description:

Job Requirements

Act as an AI Engineer responsible for designing, developing, deploying and maintaining AI-agents that drive business value. The incumbent will leverage Generative AI, Large Language Models (LLMs), Agentic AI and Microsoft AI Foundry to build scalable, secure and production-ready AI solutions.

• The role will contribute towards Agentic AI journey of the organization by combining business process understanding with agent development using modern Microsoft AI technologies (e.g. Azure AI Foundry, Microsoft Fabric and Power BI).
• Participate in the complete Agentic Development Life Cycle (AIDLC), including requirement analysis, architecture design, development, testing, deployment, monitoring and continuous improvement.
• Design, develop, test, deploy and support enterprise-grade AI agents using Azure AI Foundry, Azure OpenAI, Python, Model Context Protocol (MCP)-based integrations, orchestration workflows, prompt engineering, tool integrations, memory management and exception handling.
• Develop Retrieval-Augmented Generation (RAG) solutions by integrating enterprise knowledge sources, vector databases and Large Language Models (LLMs).
• Implement AI solutions following security, governance, responsible AI, privacy and compliance standards.
• Collaborate with data engineering teams to ensure data quality, availability and readiness for AI model development and deployment.
• Monitor performance, accuracy, reliability and usage of deployed AI solutions and drive continuous optimization.
• Create technical documentation, deployment guides, architecture documents and operational runbooks for Agentic AI solutions.
• Support user acceptance testing (UAT), production deployments, troubleshooting and ongoing enhancements.
• Participate in knowledge-sharing sessions, demonstrations and technical training programs to drive AI adoption across business functions.
• Stay updated with emerging trends in Generative AI, Agentic AI, LLMs, Multi-Agent Systems, Machine Learning, and Microsoft AI technologies.

Work Experience

Technical & Functional Skills:

• B.E. / B. Tech (Computer Science / Data Science or a related field)
• 4 years of experience in Data Science & Analytics with minimum 2 years of hands‑on experience in agent development with one end-to-end cycle of agent development from concept to design to delivery.
• Strong Experience with agent development using Azure AI Foundry, Python, AI/ML, Azure OpenAI Services, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Prompt flow & engineering andLangChain / LangGraph / CrewAI or similar technologies.
• Working knowledge of Microsoft Fabric including OneLake, Data Factory & Synapse, vector databases , MLOps and LLMOps practices including model deployment, monitoring, version control, and CI/CD pipelines.
• Understanding of REST APIs, microservices architecture and enterprise application integrations.
• Understanding of Responsible AI principles, data governance, security and compliance requirements.
• Ability to manage multiple assignments and deliver high-quality outcomes within defined timelines.