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This is a remote position. Role Overview We are looking for an AI Platform Engineer with strong experience in Azure and Snowflake to design, implement, and govern our next-generation AI capabilities. You will be responsible for enabling and operating Model Context Protocol (MCP) servers, establishing robust data and access controls, and working with multiple stakeholders to connect AI clients and agents in a secure, compliant manner. This role sits at the intersection of cloud infrastructure, data platforms, and AI enablement, and will be a key contributor to our enterprise AI strategy. Key Responsibilities 1. AI Enablement in Azure & Snowflake Design, deploy, and maintain MCP servers in Azure and Snowflake-based environments to support AI agents and developer workflows. Implement and manage data access patterns and governance controls to ensure AI systems only access permitted data (e.g., role-based access control, data masking, tokenization, and row-level security). Integrate AI capabilities with existing data pipelines, data warehouses, and lakehouse architectures in Azure and Snowflake. Collaborate with data engineering teams to ensure data quality, lineage, and observability for AI use cases. 2. Connectivity & Integration with AI Clients and Agents Work closely with application teams, data teams, and security teams to establish secure connectivity between AI clients, AI agents, and backend data/services. Define and implement APIs, connectors, and integration patterns between AI agents and enterprise systems, including identity and access management (IAM). Partner with InfoSec / Cybersecurity to ensure that AI agent connectivity adheres to organizational security standards, including network segmentation, authentication, and authorization. Troubleshoot and optimize connectivity issues between AI clients/agents and Azure, Snowflake, and other downstream systems. 3. Governance, Compliance, and Best Practices Establish guardrails for AI usage, including data classification, allowed data domains, and acceptable use of AI within the environment. Document and enforce standards, patterns, and reusable components for AI integrations across teams. Support audits and risk assessments by providing clear documentation around data flows, access controls, and AI system behavior. 4. (Optional / Nice-to-Have) Microsoft 365 & Azure MCP Enablement Configure and manage MCP servers for Microsoft 365 and Azure to support agent and developer access to enterprise content. Work with collaboration and productivity teams to integrate AI agents with M365 tools (e.g., SharePoint, Teams, OneDrive) under strict governance and compliance policies. Define policies and access controls for how agents and developers can interact with M365 and Azure resources via MCP. Required Qualifications Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. Solid hands-on experience with Microsoft Azure (e.g., Azure Data services, networking, security, identity). Practical experience with Snowflake (data modeling, security, roles, governance, integrations). Proven experience enabling or integrating AI/ML systems with enterprise data platforms (e.g., LLMs, AI agents, or similar architectures). Strong understanding of access control, security, and governance concepts (RBAC, ABAC, encryption, key management, data privacy). Experience implementing APIs, microservices, or integration frameworks to connect applications and services. Familiarity with infrastructure-as-code (e.g., Terraform, Bicep, ARM templates) and DevOps/CI-CD practices. Key Skills & Competencies Experience with Model Context Protocol (MCP) concepts, agent frameworks, or similar context/connector architectures. Hands-on work with AI platforms such as Azure OpenAI, Azure AI Studio, or other LLM platforms. Experience integrating with Microsoft 365 (SharePoint, Teams, OneDrive, Outlook) from applications or services. Familiarity with data governance tools and frameworks (e.g., Purview, Collibra, Alation) and enterprise identity (Entra ID / Azure AD). Background in enterprise security, compliance, or regulated industries (e.g., finance, healthcare, public sector). Security-first mindset, able to balance usability and risk controls. Stakeholder management: comfortable working with security, data, infra, and product teams. Originally posted on Himalayas