Agentic AI Engineer

Role & responsibilities

• 5-9 years of professional experience in a relevant field.
• Proven experience designing and developing production-grade Agentic AI and Generative AI solutions using frameworks such as LangChain and LangGraph.
• Strong understanding of agent-based architecture, orchestration, inter-agent communication (A2A protocol), and integration with the Model Context Protocol (MCP).
• Experience with evaluation frameworks (e.g., DeepEval, RAGAS) and evaluation pipeline testing, coupled with strong observability and traceability practices.
• Microsoft Certified: Azure AI Engineer Associate (AI-102) certification is mandatory.
• Strong understanding of Large Language Models (LLMs) and Small Language Models (SLMs), with solid hands-on experience in prompt engineering, Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).
• Experience with specification-driven and evaluation-driven development practices.
• Experience integrating applications with cloud services, the Microsoft Graph API, and other third-party services, as well as deploying solutions across various platforms.
• Understanding of large-scale enterprise application architecture, along with appropriate design patterns and industry best practices.
• Well-versed in Agile methodologies, with working experience using Azure DevOps, Jira, or equivalent platforms.
• Proficient in programming languages such as Python, TypeScript, or C#.

Preferred candidate profile

• Foundational understanding of Machine Learning and Deep Learning concepts.
• Hands-on experience with Azure AI Foundry and frameworks such as Microsoft Agent Framework/AutoGen, along with other Azure AI services.
• Industry-recognized AI certifications from providers such as Microsoft Azure and Anthropic.