Senior Platform Engineer Agentic AI Cogentiq

Role Summary

As a Senior Platform Engineer Agentic AI you will play a critical role in integrating deploying and scaling Cogentiq Fractals enterprise-grade agentic AI platform across complex customer environments. You will enable productionready AI agent workflows by building deep integrations deploying the platform across core runtime and governance layers and driving adoption across engineering and business teams.

This role sits at the intersection of platform engineering agentic systems and enterprise integration.

Key Responsibilities

Platform Integration & Deployment

• Integrate Cogentiq with enterprise ecosystems using MCP-based connectors APIs storage systems and business applications (ERP SaaS internal tools)
• Design and implement integrations using MCP Gateway Agent Gateway and Cogentiq SDKs
• Deploy configure and operate Cogentiq across Core Runtime (Kubernetes) and Governance layers
• Set up and maintain CI/CD pipelines for platform and agent deployments

Agent & Workflow Engineering

• Build configure and optimize AI agents and agentic workflows on the Cogentiq platform
• Implement orchestration patterns using agentic frameworks (e.g. LangGraph CrewAI)
• Translate business problems into scalable production-grade agentic solutions

Reliability Observability & Troubleshooting

• Ensure platform reliability observability security and governance compliance
• Troubleshoot integration deployment and runtime issues across distributed systems
• Collaborate closely with product platform and customer teams to resolve production issues

Enablement & Adoption

• Drive onboarding enablement and adoption of Cogentiq across internal and customer teams
• Act as a technical partner to stakeholders helping them leverage agentic AI effectively

Required Skills & Experience

• Strong Python development skills with solid OOP and system design fundamentals
• Experience building backend services using FastAPI or equivalent frameworks
• Strong API design and integration experience
• Hands-on experience with Docker and Kubernetes (K8s)
• Experience deploying platforms using CI/CD pipelines
• Deep understanding of enterprise connectors:
  • MCP-based connectors
  • APIs & SDK-based integrations
  • Storage systems and business applications
• Experience working with or integrating via Model Context Protocol (MCP)
• Strong debugging and troubleshooting skills across distributed systems
• Ability to understand business use cases and map them to practical technical solutions
• Familiarity with AI-assisted development tools (Cursor Claude Code Copilot etc.)

Nice to Have

• Exposure to LLMs RAG and model orchestration concepts
• Experience with cloud platforms (AWS Azure or GCP)
• Understanding of observability monitoring and evaluation frameworks
• Prior experience working on enterprise platforms or developer platforms

Outcomes & Impact

• Faster enterprise onboarding and platform integration
• Scalable production-ready AI agent deployments
• Increased adoption and enterprise usage of Cogentiq
• High reliability security and governance compliance in production systems