Job Description :

We are looking for an experienced Azure Agentic AI Manager to design, develop, and deploy intelligent AI agents and orchestration workflows within the Microsoft Azure ecosystem. The role will combine hands-on engineering with technical leadership to build scalable, secure, and production-ready agentic AI solutions using Azure OpenAI, Semantic Kernel, Python, RAG, and cloud-native Azure services.

The ideal candidate should have strong experience in enterprise AI/ML engineering, agentic architectures, LLM orchestration, Azure services, and production AI deployments, with the ability to lead technical initiatives and mentor engineering teams.

Key Responsibilities :

- Architect, design, and implement agentic AI workflows using Azure OpenAI Service, Semantic Kernel, and other relevant orchestration frameworks.

- Design intelligent agents capable of reasoning, tool usage, decision-making, task planning, and multi-step workflow execution.

- Develop and integrate custom tools, plugins, APIs, and enterprise connectors to extend agent capabilities.

- Build scalable RAG architectures using Azure AI Search and other vector-based retrieval technologies.

- Develop prompt engineering and prompt orchestration strategies and optimize agent behavior for accuracy, reliability, latency, and cost.

- Evaluate and fine-tune models where appropriate to improve solution performance and business outcomes.

- Deploy, monitor, and manage AI agents and services using Azure Machine Learning, Azure Kubernetes Service (AKS), Azure Functions, and related cloud services.

- Integrate AI agents with existing enterprise applications, APIs, data platforms, and business workflows.

- Establish testing and evaluation frameworks to validate AI outputs, agent behavior, reliability, and safety.

- Implement appropriate security, governance, access controls, monitoring, logging, and responsible AI practices for enterprise AI solutions.

- Lead technical design discussions, architecture reviews, code reviews, and solution workshops with engineering, architecture, product, and business stakeholders.

- Mentor and provide technical guidance to AI/ML and software engineering teams.

- Work closely with Data Engineering, Cloud, DevOps, Product, and Enterprise Architecture teams to deliver end-to-end AI solutions.

- Drive CI/CD, automation, version control, and MLOps/LLMOps practices for AI applications and models.

- Troubleshoot production issues, perform root-cause analysis, and continuously improve the reliability and performance of deployed AI solutions.

- Stay current with developments in Agentic AI, LLMs, Azure AI services, orchestration frameworks, and enterprise AI engineering practices.

Required Skills & Experience :

- 8 - 13 years of experience in software engineering, AI/ML engineering, or related technology roles, with significant hands-on experience in Generative AI.

- Strong hands-on experience building LLM-powered and Agentic AI solutions.

- Strong proficiency in Python.

- Experience with agentic/LLM frameworks such as Semantic Kernel, LangChain, AutoGen, CrewAI, or equivalent.

- Strong hands-on experience with Azure OpenAI Service and Azure AI capabilities.

- Good knowledge of Azure Cognitive Search / Azure AI Search, Azure Functions, and related Azure services.

- Strong understanding of RAG, embeddings, vector databases, prompt chaining, context management, and tool/function calling.

- Experience with vector databases such as Azure AI Search, Pinecone, Milvus, or equivalent.

- Experience deploying AI applications using Azure Machine Learning, AKS, containers, or serverless Azure services.

- Strong understanding of AI application architecture, APIs, microservices, and enterprise integration patterns.

- Experience building testing and evaluation mechanisms for LLM and agent outputs.

- Good understanding of CI/CD, MLOps/LLMOps, monitoring, observability, and production support.

- Strong understanding of cloud security, governance, identity, and enterprise application integration.

- Strong technical leadership, stakeholder management, communication, and mentoring skills.

- Ability to take AI solutions from PoC/prototype through production deployment and operational support.

Good to Have :

- Experience with Microsoft Fabric or Azure Data Factory.

- Experience with Azure AI-related certifications such as AI-102 or equivalent.

- Experience with AI model fine-tuning and evaluation methodologies.

- Exposure to responsible AI, AI governance, and enterprise security controls.

- Experience contributing to open-source AI projects or participating in AI research communities.

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical discipline.