About the Role :
We are seeking a highly skilled AI Developer with deep expertise in integrating locally trained AI / ML models into enterprise-grade production systems. The ideal candidate must have strong experience with Azure cloud infrastructure, hands-on knowledge of the Model Context Protocol (MCP), proven experience leveraging AI Foundry, and in-depth expertise in Generative AI (GenAI) for building and deploying enterprise-ready solutions.
Key Responsibilities
- Integrate and optimize locally trained AI / ML and Generative AI models within enterprise applications.
- Architect, build, and maintain scalable AI solutions using Azure services (Azure ML, Azure Kubernetes Service, Azure Functions, etc.).
- Implement and manage AI integration workflows leveraging the Model Context Protocol (MCP).
- Use AI Foundry to design, orchestrate, and operationalize AI workflows across multiple environments.
- Develop and integrate Generative AI solutions (LLMs, RAG, embeddings, fine-tuning) into production systems.
- Collaborate with cross-functional teams to design and implement AI-driven features in enterprise applications.
- Ensure enterprise-grade scalability, security, and performance in deployed AI systems.
- Work with Azure DevOps for CI / CD pipelines, monitoring, and lifecycle management of AI solutions.
- Provide technical expertise on AI model deployment, retraining workflows, and integration with enterprise applications.
Required Skills & Experience
Proven experience as an AI Developer / AI Engineer in enterprise environments.Strong hands-on experience with Azure AI / ML ecosystem (Azure Machine Learning, Azure Databricks, Cognitive Services, Azure Synapse, etc.).In-depth expertise in Generative AI (GenAI) including LLMs, RAG, embeddings, fine-tuning, and custom deployments.Hands-on expertise with Model Context Protocol (MCP) for connecting and orchestrating AI systems.Experience using AI Foundry to standardize and manage AI solutions at scale.Deep understanding of model deployment & integration (ONNX, APIs, containers, microservices).Proficiency in Python (preferred) and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).Experience with MLOps practices (CI / CD, monitoring, retraining, governance).Nice to Have
Knowledge of data pipelines using Azure Data Factory, Synapse, or Databricks.Experience in enterprise security compliance and scalable multi-tenant architectures.(ref : hirist.tech)