Senior / Lead Forward Deployed Engineer (FDE) - Cogentiq Platform

Location: Bengaluru, India

Company

hirify.global develops Cogentiq, an enterprise AI platform for building, deploying, monitoring, and governing intelligent AI agents and multi-agent workflows.

What you will do

• Design and develop production-grade AI applications using the Cogentiq platform.
• Deploy enterprise AI solutions from proof of concept through production.
• Build integrations with SAP, Salesforce, Workday, ServiceNow, SharePoint, Snowflake, Databricks, and other business systems.
• Develop reusable connectors, APIs, SDKs, MCP servers, and automation components.
• Design secure, scalable cloud-native architectures and reliable backend services and microservices.
• Work directly with customer engineering and business teams through requirements discovery, technical workshops, troubleshooting, and deployment optimization.

Requirements

• Strong software engineering fundamentals and proficiency in Python; experience with Java or TypeScript is a plus.
• Experience building production-grade backend applications, REST APIs, microservices, distributed systems, and database-backed services.
• Hands-on experience with LLMs, agentic AI frameworks, RAG, tool calling, prompt engineering, and AI evaluation.
• Experience with enterprise integrations, API-first architectures, OAuth, JWT, SAML, SQL, REST, and GraphQL APIs.
• Experience deploying cloud-native applications on Azure, AWS, or Google Cloud with Docker and Kubernetes, including CI/CD, messaging, observability, monitoring, and security practices.
• Ability to lead architecture discussions and technical workshops and translate evolving customer requirements into reusable technical solutions.

Nice to have

• Experience with the Model Context Protocol and modern AI orchestration frameworks.
• Experience with Java or TypeScript.

Culture & Benefits

• Join the founding engineering team building Fractal's flagship enterprise AI platform.
• Work with generative AI, agentic AI, enterprise context, MCP, knowledge graphs, and AI governance.
• Solve complex enterprise challenges and influence both customer outcomes and platform direction.
• Build reusable capabilities for enterprise AI deployments across industries.