Role Summary

As a Senior Platform Engineer – Agentic AI, you will play a critical role in integrating, deploying, and scaling Cogentiq, Fractal’s enterprise-grade agentic AI platform, across complex customer environments. You will enable production‑ready 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