AI Engineer

This role is for one of Weekday’s clients

Min Experience: 10+ years

Location: Bengaluru and Palo Alto

Department: Engineering Employment
JobType: full-time

We are looking for a highly experienced Senior AI Engineer to design, develop, and operate the backend and AI infrastructure required to run intelligent AI agents securely, efficiently, and reliably in production. This role combines hands-on AI engineering with strong backend development, platform engineering, and production operations.

You will work across LLM-powered applications, AI agents, model routing, tool integrations, inference services, multi-agent workflows, and evaluation frameworks. You will be responsible for transforming early-stage prototypes into scalable, observable, secure, and production-ready services.

The ideal candidate has strong proficiency in Python, practical experience building AI applications and agentic systems, and a solid understanding of the Model Context Protocol (MCP). Experience with Rust-based services, cloud infrastructure, and AI platform engineering will be valuable.

Requirements

Key Responsibilities

AI and Agentic Engineering

• Design, build, and deploy production-grade AI applications and agent infrastructure using Python.
• Develop agent workflows involving tool calling, memory, retrieval-augmented generation (RAG), planning, and multi-agent collaboration.
• Integrate hosted and self-hosted language models, inference endpoints, and enterprise systems.
• Build MCP servers, clients, tools, and gateway integrations to enable secure and governed access to external tools and data.
• Implement structured outputs, retries, fallbacks, error recovery, and human approval workflows.
• Improve AI system performance through evaluation, experimentation, tracing, and continuous feedback.

Backend and Platform Engineering

• Develop scalable APIs, services, asynchronous workers, and backend components for AI-powered systems.
• Design reusable abstractions for models, agents, tools, workflows, policies, and execution environments.
• Build event-driven services for long-running AI workloads.
• Work with PostgreSQL, Redis, message queues, object storage, and vector databases.
• Contribute to Rust-based platform services and architectural decisions where required.
• Write maintainable, well-tested code with clear interfaces and strong operational visibility.

AI Infrastructure and MLOps