Senior Platform Engineer: Agentic AI - Cogentiq

Fractal; Bengaluru; On-site; INR 1,000,000 - 2,000,000; Full time

Job summary

Fractal is looking for a Senior Platform Engineer to play a vital role in integrating and scaling Cogentiq, their enterprise-grade agentic AI platform. The position involves building deep integrations, deploying the platform, and ensuring production-ready workflows.

The ideal candidate will have strong Python skills, experience with Docker and Kubernetes, and the ability to design scalable solutions to business problems. You will help drive the adoption of Cogentiq across teams and ensure platform reliability.

Qualifications

• Strong Python development skills with solid OOP and system design fundamentals.
• Experience building backend services using FastAPI or equivalent frameworks.
• Hands-on experience with Docker and Kubernetes.

Responsibilities

• Integrate Cogentiq with enterprise ecosystems using APIs and storage systems.
• Deploy, configure, and operate Cogentiq across Core, Runtime, and Governance layers.
• Collaborate closely with product and customer teams to resolve production issues.

Skills

Python development skills

API design and integration experience

Docker and Kubernetes experience

Experience with CI/CD pipelines

Debugging skills across distributed systems

Tools

FastAPI

AWS

Azure

GCP

Job description

About Cogentiq (Fractal’s Agentic AI Platform)

Cogentiq is Fractal’s secure, scalable, enterprise agentic AI platform that enables teams to build, test, deploy, monitor agents and multi‑agent workflows with strong observability, evaluation, guardrails and RBAC across any cloud or LLM framework. It includes a no/low‑code development console, Agent & MCP Gateways, and an enterprise marketplace for reusable agents, tools, connections and guardrails.

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.

Key Responsibilities

• 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
• 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
• 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
• 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