Arishi - Artificial Intelligence Engineer - LLM/RAG

About The Role

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.