Lead AI Software Engineer

About this role: We are seeking an AI Engineer to help design, build, and deliver next-generation AI-enabled applications and agentic systems. This role combines hands-on engineering across frontend applications, Python backend services, MCP infrastructure, agent workflows, and secure enterprise integrations.   As an AI Engineer, you will build user-facing applications and backend services that integrate with  Model Context Protocol   ( MCP )   servers, LLM-based applications, RAG-based retrieval systems, and agent orchestration platforms .   You will work closely with AI developers, platform engineers, product teams, and other stakeholders to deliver scalable, secure, observable, and production-ready AI solutions.   This engineering role is suited for candidates who can contribute across the full stack while also helping shape reusable patterns for MCP-enabled tools, skills, and agent-driven workflows.   In this role you will   Design, build, and maintain  AI-enabled applications  using React, TypeScript, Python, and modern API frameworks.   Develop    frontend applications  aligned with enterprise UI standards and reusable component patterns.   Build and maintain  Python backend services, APIs, MCP services, and integration layers  using frameworks such as   MCP,   FastAPI   &   Flask.   Design, implement, and integrate with  MCP servers and MCP clients  that enable secure context sharing between models, tools, applications, and agent runtimes.   Develop and support   agentic workflows   that execute   complex, multi-step tasks using frameworks such as LangChain Deep Agents, Google ADK, or similar agent frameworks.   Build  skill-based and modular agents  that decompose capabilities into reusable, composable, and versioned skills.   Build reusable components, services, tool schemas, and integration patterns that can be shared across enterprise AI applications.   Integrate logging, tracing, and observability frameworks, including   Arize &   Splunk   compatible logging and agent observability tooling.   Ensure applications and services are ready for deployment on enterprise container platforms such as OpenShift or Kubernetes.   Collaborate with AI developers, platform engineers, product teams, and architecture partners to deliver robust and scalable AI solutions.

Deliver end-to-end AI application features  using MCP services, and agent orchestration layers .   Own and contribute to  MCP service implementations , including tool schema design, tool registration, API integration, authorization enforcement, and deployment readiness.   Build and maintain   MCP-compatible tools, skills, APIs, and UI components  for use across AI applications and agent runtimes.   Integrate AI-powered user interfaces with  backend APIs, MCP servers, RAG systems, and agent workflows .   Implement   RBAC   policies governing which users, agents, clients, and applications can invoke specific tools or services.   Develop agent workflows that support complex user objectives through  mcp &   skill   orchestration .   Align solutions with enterprise platform standards, including application scaffolding, security, observability, logging, testing, and deployment practices.   Ensure frontend applications are performant, accessible, maintainable, and aligned with reusable design patterns.   Ensure backend and AI services are scalable, observable, secure, and ready for deployment on OpenShift or Kubernetes-based environments.   Write and maintain technical documentation, integration guides, API specifications, and operational runbooks.   Contribute to engineering best practices, including code reviews, automated testing, CI/CD, monitoring, and production support readines   Required qualifications   Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience,