Lead Software Engineer - Machine Learning

Responsibilities Agentic Framework Implementation: Collaborate with AI Researchers and Data Scientists to translate complex reasoning loops (e.g., ReAct, Reflection, Tree of Thoughts) and experimental algorithms into clean, production-grade code. End-to-End Agent Architecture: Design, build, and manage comprehensive agent pipelines, including state management, long/short-term vector memory systems, automated tool-calling integrations, and dynamic feedback loops. High-Performance Service Delivery: Develop and deploy extensible, scalable API microservices optimized to minimize latency during heavy token generation, parallel agent execution, and high-concurrency traffic loads. Operational Telemetry & Evaluation: Design and implement robust tracking frameworks to monitor agent drift, hallucination rates, cost/token efficiency, and multi-step execution accuracy to ensure systemic reliability. Strategic Ecosystem Collaboration: Architect foundational AI primitives from scratch and liaise with cross-product architects to ensure seamless integration of agentic capabilities into existing product ecosystems. Prototyping & LLM Benchmarking: Lead Proof of Concept (POC) initiatives utilizing diverse tech stacks, open-source orchestrators (e.g., LangGraph, AutoGen, CrewAI), and custom vector infrastructures to validate optimal solutions for complex business workflows. Lifecycle Ownership: Independently own the full lifecycle of feature delivery—from alignment with product teams on agent objectives to final production deployment, guardrail reinforcement, and continuous optimization. Skills & Competencies Production Agentic Engineering: Proven capability in translating raw LLMs/LMMs into reliable, stateful, and autonomous software applications with deterministic guardrails. LLMOps & Agent Evaluation: Deep expertise in lifecycle management for generative models, including fine-tuning, retrieval-augmented generation (RAG) optimization, prompt versioning, and routing architectures. Distributed State & API Design: Strong distributed systems architecture skills, specifically in managing asynchronous workflows, message queues, and low-latency API microservices required for multi-agent coordination. Cognitive Telemetry: Proficiency in establishing monitoring frameworks for tracking agent reasoning paths, tool execution success rates, API cost metrics, and user-intent alignment. Technical AI Leadership: Ability to execute rapid prototyping with emerging AI stacks, benchmark foundation models, evaluate vector databases, and drive cross-functional alignment.

Qualifications Experience: 6–9 years of professional experience in software engineering and machine learning development, with recent hands-on experience deploying LLM-based or agentic systems into production. Track Record: A proven history of successfully building, productionizing, and maintaining large-scale Machine Learning solutions and scalable backend architectures. Education: Degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field. At Freshworks, we have fostered an environment that enables everyone to find their true potential, purpose, and passion, welcoming colleagues of all backgrounds, genders, sexual orientations, religions, and ethnicities. We are committed to providing equal opportunity and believe that diversity in the workplace creates a more vibrant, richer environment that boosts the goals of our employees, communities, and business. Fresh vision. Real impact. Come build it with us.

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.