Job Description

QUICK FACTS

EngagementZensar at Client (Client Site)LocationHybrid / On-site — Client Engineering HubsSeniorityMid to Senior (4–8 years)EmploymentFull-Time Contract with conversion pathGrowth PathMCP Build → Internal AI Platform Engineering

KEY RESPONSIBILITIES

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

NICE TO HAVE

• 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

SKILLS AT A GLANCE

Python

FastAPI

OAuth 2.0

MCP Protocol

REST APIs

Docker / K8s

Claude API

Platform Eng

QUICK FACTS

EngagementZensar at Client (Client Site)LocationHybrid / On-site — Client Engineering HubsSeniorityMid to Senior (4–8 years)EmploymentFull-Time Contract with conversion pathGrowth PathMCP Build → Internal AI Platform Engineering

KEY RESPONSIBILITIES

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

NICE TO HAVE

• 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

SKILLS AT A GLANCE

Python

FastAPI

OAuth 2.0

MCP Protocol

REST APIs

Docker / K8s

Claude API

Platform Eng

QUICK FACTS

EngagementZensar at Client (Client Site)LocationHybrid / On-site — Client Engineering HubsSeniorityMid to Senior (4–8 years)EmploymentFull-Time Contract with conversion pathGrowth PathMCP Build → Internal AI Platform Engineering

KEY RESPONSIBILITIES

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

NICE TO HAVE

• 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

SKILLS AT A GLANCE

Python

FastAPI

OAuth 2.0

MCP Protocol

REST APIs

Docker / K8s

Claude API

Platform Eng

At Zensar, we’re “experience-led everything” We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus

Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.

We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.