Agent Engineer

Role: Agent Engineer

Experience: 3 - 6 years

Location: Bengaluru

Role Summary

We are building an AI-native B2B intelligence platform. You will build our core Agentic Harness a performant, configurable agent layer that powers everything from our database search to sourcing and selling agents. This is a hands-on builder role focused on system design, observability, and agent performance.

Roles & Responsibiliti

• Build a lightweight, scalable agent harness that allows the business to configure and deploy multiple domain-specific agents.
• Design the core orchestration loop, including memory management, state handling, tool calling, and deterministic guardrails.
• Ensure the agent layer is model-agnostic, easily swappable, and testable across different commercial and open-source LLMs.
• Optimize agent performance for low latency, efficient token usage, and minimal memory footprint.
• Establish robust evaluation frameworks (evals) to measure agent reasoning, tool selection accuracy, and task completion.
• Collaborate with the research team to tune open-source LLMs specifically for optimal agentic behavior and tool use.

Skills Required

Must Have:

• 3+ years of experience in backend or ML engineering, with strong Python skills.
• Experience building agentic workflows or orchestration loops
• Deep understanding of LLM application architecture, state machines, and API integrations (FastAPI, PostgreSQL).
• Strong systems engineering mindsetyou treat agent development as reliable software engineering, not just prompting.
• Experience benchmarking, profiling, and optimizing LLM performance and latency.

Good to Have:

• 3+ years of experience in backend or ML engineering, with strong Python skills.
• Experience building agentic workflows or orchestration loops
• Deep understanding of LLM application architecture, state machines, and API integrations (FastAPI, PostgreSQL).
• Strong systems engineering mindsetyou treat agent development as reliable software engineering, not just prompting.
• Experience benchmarking, profiling, and optimizing LLM performance and latency.