Agentic AI Engineer

Job Summary:
We are seeking a highly skilled Agentic AI Engineer to design, develop, and deploy intelligent AI agents using Azure AI Foundry and modern AI agent frameworks. The ideal candidate will have strong software engineering experience, hands-on expertise in LLM-powered applications, and a proven ability to build scalable, production-grade multi-agent systems in cloud-native environments.

Key Responsibilities:
Design, develop, and deploy intelligent AI agents using Azure AI Foundry and Azure OpenAI.
Build scalable, production-grade multi-agent systems for enterprise automation.
Develop AI-powered applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering techniques.
Implement agent orchestration using frameworks such as AutoGen, Semantic Kernel, LangChain, or similar Agent SDKs.
Develop RESTful APIs, microservices, and event-driven applications to support AI solutions.
Integrate AI agents with enterprise systems, databases, APIs, and SaaS platforms.
Deploy and manage AI workloads in cloud-native environments using Azure services.
Collaborate with cross-functional teams to design, develop, test, and optimize AI solutions.
Implement CI/CD pipelines and DevOps best practices for AI application deployment.
Containerize applications using Docker and deploy them on Kubernetes-based environments.
Monitor, troubleshoot, and optimize AI agent performance in production.

Required Qualifications:
8+ years of experience in software engineering with strong cloud-native application development experience.
Strong programming skills in Python (preferred) or Java.
Hands-on experience with Azure AI Foundry and Azure OpenAI.
Experience developing AI agents using frameworks such as:
AutoGen
Semantic Kernel
LangChain
Agent SDKs

Preferred Qualifications:
Experience with Retrieval-Augmented Generation (RAG) and Vector Databases (Pinecone, Chroma, Weaviate, Qdrant, FAISS).
Hands-on experience with Prompt Engineering, Context Engineering, and LLM optimization.
Experience with Docker, Kubernetes, and containerized deployments.
Familiarity with CI/CD pipelines using Azure DevOps, GitHub Actions, or Jenkins.
Experience with REST APIs, Microservices, and Event-Driven Architectures.
Knowledge of DevOps, MLOps, or LLMOps best practices.
Experience integrating AI agents with enterprise applications, databases, and SaaS platforms.
Experience working with Azure Cloud Services and cloud-native application development.
Azure certifications such as Azure AI Engineer Associate (AI-102) or Azure Solutions Architect Expert are a plus.
Excellent analytical, problem-solving, communication, and collaboration skills.