Agentic AI Engineer

Job Summary

Client is seeking a highly skilled Senior Agentic AI Engineer to design, develop, and deploy modern Agentic AI solutions. This role focuses on building production-grade Generative AI applications, multi-agent workflows, and Retrieval-Augmented Generation (RAG) pipelines for enterprise use on Microsoft Azure.

You will collaborate with architects, software engineers, data engineers, and business stakeholders to translate requirements into AI-powered software solutions. The ideal candidate brings hands-on experience developing, evaluating, and operating Agentic AI solutions, supported by strong front-end and back-end engineering fundamentals.

Major Responsibilities

• Build Generative AI & Retrieval-Augmented Generation LLM Applications
• Build LLM-powered applications for text generation, summarization, Q&A, conversational AI, enterprise knowledge search, and multi-agent orchestration.
• Develop advanced RAG pipelines using embeddings, Azure AI Search vector and hybrid retrieval, document chunking, metadata filtering, reranking, citations, and grounding techniques with enterprise data.
• Build secure, reliable integrations between AI agents and enterprise tools, REST APIs, relational databases, and event-driven services.
• Develop and maintain user-facing AI application experiences using React and TypeScript, and supporting application services using Node.js or comparable back-end technologies.

AI Agents & Agentic Automation

• Design and implement single-agent and multi-agent systems for intelligent automation, decisioning, and complex workflows.
• Build autonomous and human-in-the-loop agents that plan, reason, act, and interact with tools, APIs, enterprise data, and event-driven systems.
• Develop agentic workflows using Microsoft Agent Framework, Azure AI Foundry services, or comparable modern orchestration frameworks.
• Implement configuration-driven agent behavior, prompt and tool management, authorization boundaries, and resilient error-handling patterns.
• Define and automate evaluation approaches for agent quality, including groundedness, relevance, citation quality, safety, and regression testing.
• Instrument agent workflows for traces, tool calls, latency, token usage, errors, and operational metrics using OpenTelemetry, Application Insights, or comparable observability platforms.
• Build highly scalable, secure, containerized solutions with CI/CD, health checks, horizontal scaling, and production monitoring.

Education and Experience Requirements:

• Requires a bachelors degree (or international equivalent) and 8+ years of relevant software engineering experience.
• 2-3 years of hands-on Generative AI, LLM application, or Agentic AI solution development experience.
• Strong software engineering background with experience designing and deploying production-grade cloud applications.
• Experience building front-end applications with React and TypeScript, and back-end services with Node.js or comparable application frameworks.
• Hands-on experience building Generative AI and RAG applications with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector or hybrid search, knowledge retrieval, grounding, and citations.
• Experience with Agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable orchestration frameworks; including single-agent and multi-agent systems, tool-calling workflows, and human-in-the-loop controls.
• Experience evaluating and improving agent quality, including prompt engineering, test datasets, LLM-based evaluation, safety checks, and production feedback loops.
• Strong knowledge of LLMOps, CI/CD, containerization (Docker and Kubernetes), observability, and production operations for AI applications.
• Good understanding of RESTful API principles, asynchronous application patterns, secure integrations, relational databases, SQL, and data-access patterns; familiarity with SQL/NoSQL data stores and data engineering or ETL pipelines.
• Experience working in an enterprise environment with large-scale, secure AI deployments, including identity, authorization, data privacy, compliance, and production monitoring.
• Strong analytical, problem-solving, collaboration, and communication skills.