Engagement
Zensar at Client (Client Site)
Location
Hybrid / On-site — Client Engineering Hubs
Seniority
Mid to Senior (4–8 years)
Employment
Full-Time Contract with conversion path
Growth Path
MCP Build Internal AI Platform Engineering
MCP Server Design & Development
Design and implement MCP (Model Context Protocol) servers in Python, exposing enterprise tools and internal APIs as Claude-accessible resources and tool calls
Build MCP integrations for Client's existing internal stack — Jira, GitHub, Confluence, Salesforce, internal data APIs, and custom microservices
Implement both SSE (HTTP/streaming) and stdio transport modes depending on deployment context, and advise teams on when to use each
Design robust tool schemas — well-defined input/output contracts, clear tool descriptions that guide Claude's reasoning and usage
Write test suites for MCP servers — unit tests, integration tests with MCP Inspector, and end-to-end validation with Claude Desktop
Authentication, Security & API Integration
Implement OAuth 2.0 flows (Authorization Code, Client Credentials, PKCE) for secure MCP server authentication — following the MCP authorization spec
Integrate with identity providers (Okta, Azure AD, Google) to enable SSO-based access control on MCP servers
Design and implement API gateway patterns for MCP backends — rate limiting, scoped token management, audit logging
Ensure MCP servers meet enterprise security standards — secrets management (Vault, AWS Secrets Manager), TLS, least-privilege access
Build adapters for REST, GraphQL, and gRPC-based internal APIs, abstracting complexity behind clean MCP tool interfaces
Platform Engineering (Growth Path)
Contribute to the design of Client's internal AI platform — a shared infrastructure layer for deploying, discovering, and managing MCP servers at scale
Build developer-facing tooling: CLI utilities, SDK wrappers, scaffolding templates that make it fast for Client engineering teams to build new MCP integrations
Implement observability for the MCP layer — structured logging, distributed tracing, dashboards (Datadog, Grafana) to monitor AI tool usage across teams
Design multi-tenant MCP deployment patterns — namespace isolation, per-team credential scoping, usage quotas
Work with Client's platform team to containerize and deploy MCP servers on Kubernetes, with CI/CD pipelines and GitOps workflows
Collaboration & Enablement
Act as the technical MCP subject-matter expert for Client's engineering teams — running office hours, reviewing integration designs, unblocking builders
Collaborate with Endpoint AI Support Engineers (Role ZEN-RBK-ENG-01) to ensure seamless end-to-end experience from user machine to MCP server
Write technical documentation, integration guides, and architecture decision records (ADRs) for all MCP infrastructure
Participate in Client's AI working group — contributing insights from the integration layer to shape overall AI strategy
REQUIRED SKILLS & EXPERIENCE
Backend Engineering
4+ years of Python backend development — FastAPI, Flask, or similar async frameworks; clean, testable, production-grade code
Strong REST API design skills — resource modeling, HTTP semantics, versioning, pagination, error standards (RFC 7807)
Experience consuming and building integrations with third-party APIs (SaaS platforms, internal microservices)
Proficiency with async Python (asyncio, httpx) — critical for MCP server performance
Node.js/TypeScript familiarity is a strong plus — the MCP SDK has first-class TypeScript support
Authentication & Security
Deep understanding of OAuth 2.0 — grant types, token introspection, refresh flows, scopes
Experience integrating with OAuth/OIDC identity providers in production: Okta, Azure AD, or Google Workspace
JWT handling — signing, validation, claims inspection, expiry management
Secure secrets management — environment variables, secrets vaults, never hardcoded credentials
Infrastructure & DevOps
Containerization with Docker — writing production Dockerfiles, multi-stage builds, image optimization
Kubernetes basics — Deployments, Services, ConfigMaps, Secrets, Ingress; comfortable reading and writing YAML manifests
CI/CD experience — GitHub Actions, GitLab CI, or similar; automated testing and deployment pipelines
Cloud-native mindset — AWS, GCP, or Azure; familiarity with managed services (Lambda, Cloud Run, ECS)
AI & MCP Ecosystem
Working knowledge of MCP (Model Context Protocol) — understanding of the protocol primitives: tools, resources, prompts, sampling
Experience with the Anthropic Python SDK or Claude API — making API calls, handling streaming responses, function calling/tool use
Awareness of LLM integration patterns — prompt engineering basics, context management, tool result handling
Familiarity with agent frameworks (LangChain, LlamaIndex, or similar) is a plus
Prior experience building MCP servers — even personal/open-source projects are highly valued
Contributions to open-source MCP server repositories or the MCP spec discussion
Background in developer tooling, internal platforms, or API gateway products
Experience at a SaaS or security company (highly relevant given Client's domain)
GraphQL API design and federation
Familiarity with Anthropic's Claude system prompt design and tool-use best practices
