Agentic AI Engineer

Build India’s sovereign AI stack for a billion people and shape the future of technology

Job Summary

We are looking for an Agentic AI Engineer to join our growing team to design and develop agentic AI systems that can plan, reason, and act. This includes single- and multi-agent AI systems and orchestration of workflows that involve retrieval augmented generation (RAG), contextual awareness, reasoning, tool calling, and inter-agent communication. This role is ideal for software engineers with AI/ML development experience, and the passion for transforming generative AI models into actionable, goal-driven systems capable of solving complex, real-world business problems.

Key Responsibilities

Architect and build agentic AI systems that integrate agents with foundational generative AI models, third-party tools and enterprise systems, and APIs, using existing agentic frameworks or custom-built orchestrators.
Build and maintain retrieval-augmented generation (RAG) and reasoning pipelines to ground agent decisions in reliable, real-world data, and enable persistent and adaptive agent behavior.
Optimize orchestration and reasoning performance, balancing autonomy, interpretability, and reliability.
Collaborate with Gen AI and application engineers, ML Ops, and product teams to deploy agentic AI systems in production.
Monitor and benchmark agent performance and ensure that our AI systems are safe, accurate, trustworthy, and deliver an elegant user experience.
Document agent architectures, communication flows, guard rails, context engineering, and orchestration logic to ensure reproducibility and clarity.
Stay current with advances in multi-agent orchestration, RAG, cognitive architectures, and AI safety mechanisms.
Collaborate with cross-functional teams to deliver complete solutions for our customers.

Minimum Qualifications and Experience

Required Expertise

Proficiency in Python and agentic frameworks such as LangGraph, DSPy, AutoGen, CrewAI, etc.
Experience with APIs and agent communication protocols such as Model Context Protocol (MCP), Agent Communication Protocol (ACP), and Agent-to-Agent (A2A).
Experience in prompt design, context engineering, and integrating AI agents with multimodal foundational AI models for reasoning, planning, and dialogue.
Working knowledge of vector databases and retrieval augmented generation (RAG).
Understanding of context windows, memory graphs, and long-term reasoning strategies in agentic systems.
Solid foundation in computer science fundamentals such as data structures, algorithms, programming, design patterns, virtualization, etc., and strong problem-solving skills.
Having high standards of code quality (clean, well-documented, modular, maintainable, reliable, efficient, secure coding) and automated testing.
Team player with excellent interpersonal skills and ability to collaborate effectively with remote team members.
Go-getter attitude and ability to flourish in a fast-paced, startup environment.