Senior AI Engineer, MCP
We are seeking a Senior AI Engineer, MCP to lead the design and evolution of a next-generation AI Gateway platform that enables secure, scalable, and governed access to Large Language Models (LLMs) and agentic workflows across the enterprise in a SAAS environment. This role owns the end-to-end architecture covering MCP (Model Context Protocol), agents, multi-provider LLM integrations, authentication and authorization, semantic caching, rate limiting, guardrails, and reusable frameworks for agent and tool development. The Engineer will work closely with multiple product teams that integrate with the gateway as part of their agentic workflows. This is a hands-on architecture role requiring strong technical depth, clear problem definition, prototyping, and cross-team technical influence.
Responsibilities
- Develop the AI Gateway platform, including MCP, agents, LLM routing, governance, and observability
- Translate business and product requirements into clear problem statements, architectural designs, and scalable technical solutions
- Design extensible frameworks for MCP server capabilities and integrations, as well as agent orchestration and reusable agent skills
- Architect gateway capabilities such as semantic caching for LLM responses, rate limiting, quotas, and traffic governance
- Build guardrails and validation layers for safe LLM/MCP usage
- Design and review authentication and authorization models, including multiple OIDC-based flows, identity propagation, and token-based access control for MCP and LLM traffic
- Build prototypes and reference implementations to validate architectural decisions and guide engineering teams
- Partner with multiple product teams integrating with the AI Gateway, providing architecture guidance, best practices, and integration patterns
- Review designs and code, providing actionable feedback to ensure quality, performance, scalability, and security
- Define and review CI/CD best practices using modern GitHub-based pipelines
- Architect and review Kubernetes-based deployment models, ensuring scalability, resiliency, and production readiness
Requirements
- 3+ years of software engineering experience, with proven experience designing and delivering large-scale distributed systems or platform products
- Proficiency in Golang and Python
- Familiarity with AI centric development with strong emphasis on quality gates and architecture patterns to enforce agentic behavior in producing quality code
- Understanding of AI gateways, AI/LLM platforms, or middleware systems, including routing, governance, and scalability
- Experience with microservices and service-oriented architectures for multi-tenant SAAS environments, PostgreSQL for transactional data, and Redis for caching and distributed coordination
- Background in authentication and authorization systems, particularly OAuth-based approaches
- Demonstrated ability to independently drive problem definition, architecture, prototype, and execution guidance
- Experience designing platforms and frameworks used by multiple product teams
Nice to have
- Experience with MCP (Model Context Protocol), agentic platforms, or AI orchestration frameworks
- Knowledge of working with EDA software
- Prior work on LLM gateways, API gateways, or AI middleware platforms
- Experience building generic developer frameworks or SDKs adopted across teams
- Familiarity with guardrails, semantic caching, prompt/response optimization, and LLM cost control techniques
- Experience operating high-throughput, low-latency systems in Kubernetes environments
- Strong written and verbal communication skills for cross-team architectural alignment
