About the RoleWe are looking for an AI Integration Engineer to help build and operationalize a governed Enterprise AI Platform on AWS. The role will focus on integrating AI agents with enterprise applications, implementing Model Context Protocol (MCP) integrations, developing agent workflows, and ensuring secure, governed access to enterprise systems.

You will work closely with AI Platform Engineering, Cloud, Security, DevOps, and business teams to onboard production-ready AI use cases.

Key Responsibilities

- Design, develop, and implement MCP-based integrations between AI agents and enterprise applications.
- Integrate AI agents with systems such as:
- ServiceNow
- Microsoft 365
- SAP SuccessFactors
- Legal/document repositories
- Internal enterprise APIs and future business applications
- Build and maintain agent workflows using AWS AgentCore and Amazon Bedrock.
- Implement secure tool and API integrations for AI agents.
- Configure and integrate AI guardrails, policies, identity, and access controls.
- Implement authentication and authorization using AWS IAM and enterprise identity platforms.
- Support secure agent-to-system communication while enforcing enterprise security and compliance requirements.
- Develop reusable integration patterns and frameworks for onboarding new enterprise systems.
- Troubleshoot integration, API, authentication, agent workflow, and connectivity issues.
- Work with platform teams to implement logging, monitoring, tracing, and auditability.
- Integrate applications with Amazon CloudWatch, AWS CloudTrail, and OpenTelemetry for operational visibility.
- Support implementation of kill-switch and policy-enforcement mechanisms for AI agents.
- Collaborate with DevOps teams on CI/CD,



version control, automated testing, and deployment of integrations.
- Support Infrastructure as Code and automation where required.
- Participate in onboarding business-aligned AI use cases onto the enterprise platform.
- Create technical documentation, integration standards, runbooks, and knowledge-transfer material.
- Work with security and governance teams to ensure integrations comply with enterprise policies.

Required Skills
- 4+ years of experience in software integration, cloud engineering, API development, platform engineering, or a related role.
- Hands-on experience with AWS services, particularly:
- Amazon Bedrock
- AWS IAM
- Amazon CloudWatch
- AWS CloudTrail
- AWS Lambda
- API Gateway
- S3
- Experience with AI/LLM integrations, AI agents, or GenAI applications.
- Strong understanding of Model Context Protocol (MCP) and MCP-based tool/server integrations.
- Experience developing and integrating REST APIs and enterprise applications.
- Strong programming experience in Python, TypeScript, or JavaScript.
- Understanding of authentication and authorization mechanisms such as OAuth 2.0, OIDC, JWT, API keys, and IAM.
- Experience integrating SaaS platforms or enterprise applications.
- Understanding of microservices, APIs, webhooks, event-driven architecture, and distributed systems.




- Experience with Git and CI/CD pipelines.
- Understanding of logging, monitoring, tracing, and application observability.

Good to Have
- Experience with AWS AgentCore.
- Hands-on experience with Amazon Bedrock Agents / Knowledge Bases / Guardrails.
- Experience with MCP servers, MCP clients, and custom MCP tools.
- Experience integrating ServiceNow, Microsoft 365, SuccessFactors, SAP, or legal/document management platforms.
- Knowledge of OpenTelemetry.
- Experience with Terraform, AWS CDK, or CloudFormation.
- Experience with Kubernetes/EKS.
- Understanding of AI security, prompt injection, data leakage, and AI governance.
- Experience with enterprise IAM/SSO platforms.
- Familiarity with Kiro, Quick Suite, or citizen-developer AI enablement.
- Experience working in regulated or enterprise environments.

Key Deliverables
- Production-ready MCP integrations for enterprise applications.
- Reusable AI agent integration frameworks and patterns.
- Secure agent workflows with appropriate identity, access controls, and guardrails.
- Integration observability, logging, and audit trails.
- Automated deployment and versioning of integration components.
- Successful onboarding of business AI use cases.
- Technical documentation and knowledge transfer to the internal AI platform team.

Ideal Candidate Profile
- The ideal candidate combines AI/LLM integration knowledge with strong API and AWS engineering skills. They should be comfortable working across AI agents, enterprise applications, cloud infrastructure, security, and DevOps, with a robust focus on building secure, reusable, and production-ready integrations.

📌 AI Integration Engineer (India)
🏢 Kansoft
📍 India