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Mid-level AI role, metro location, broad skillset, and common title increase competition.
Enterprise AI integration and MCP expertise make cross-industry transfer difficult.
Many mandatory technologies plus explicit 5-8 years requirement creates high shortlisting rigidity.
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Design, deploy, and operationalize secure MCP-based AI tool ecosystems integrating LLMs with enterprise applications, APIs, and data sources.
Lead integration development across platforms such as Azure, ServiceNow, Databricks, Microsoft 365, and Jira to support enterprise AI deployments.
Define and implement standards for tool discovery, access control, authentication, governance, and establish observability and performance optimization for AI ecosystems.
5-8+ years of relevant work experience in AI integration or enterprise AI deployment roles.
Proficiency in Python, TypeScript, MCP, REST APIs, OAuth, Azure, Kubernetes, API gateways, and enterprise security.
Experience with designing and deploying AI tool ecosystems and handling DevOps and observability practices.
Location requirement: Mumbai, India.
Strong expertise in enterprise AI integration architectures and MCP-based systems with hands-on experience deploying secure, scalable AI toolchains.
Ability to collaborate closely with AI architects and business teams to productionize enterprise AI use cases effectively.
Experienced in cloud engineering and governance, with knowledge of multi-tenant architectures and AI platform acceleration frameworks.