Python AI Engineer

Role overview

Python AI Engineer at Techolution

Responsibilities

• Design, build, and scale backend services for PR Genie, PRR Genie, Cognitive Assessment, and Workflow Composer.
• You will develop solutions across web platforms and our VS Code extension, leveraging real-time communication patterns.

Employment details

• Location: Remote [India]

About the employer

• At Techolution, the world's leading Full Enterprise Automation specialist, we're seeking an exceptional Associate Python AI Engineer to architect the backend that powers how we ship software.

Additional role details

• Are you a Python engineer who thrives on building scalable AI solutions from the ground up?
• You'll own critical production systems like PR Genie and PRR Genie, impacting 150-200 daily pull requests, and contribute to our mission of delivering $900M+ in AI ROI.
• If you're passionate about end-to-end ownership, deep problem-solving, and transforming enterprise AI, this is your opportunity to make a tangible impact and shape the future of automation.
• This role requires end-to-end ownership of features, from architectural design to production deployment.
• Exhibit profound ownership, ensuring system stability and possessing the expertise to diagnose and resolve critical issues efficiently.
• Strong, production-grade Python is the foundation everything else rests on.
• Solid OOPS design.
• Hands-on FastAPI and Flask.
• Real experience with RAG and a point of view on how to scale it.
• WebSocket and Server-Sent Events for real-time systems, with GraphQL a strong plus for our extension layer.
• A working understanding of transformer architecture.
• And DSA fundamentals sharp enough that debugging a live system feels natural, not intimidating.
• Agent orchestration and prompt engineering are a plus, but honestly, we will teach you how we do those here.
• What we cannot teach is the instinct to own a hard system and make it scale.
• Bring that.
• Familiarity with Cloud Computing Fundamentals (AWS/GCP/Azure) for deployment is beneficial.
• Experience with Containerization (Docker) for packaging AI applications is a plus.
• Understanding of MLOps Concepts for managing the ML lifecycle is advantageous.