Lead Software Engineer - Machine Learning

Google Chrome Microsoft Edge Apple Safari Mozilla Firefox Lead Software Engineer - Machine LearningFull-time Compensation: INR 0 - INR 0 - yearlyCompany DescriptionOrganizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives. To create a better experience for their customers and employees. To help them grow. Software is a choice that can make or break a business. Create better or worse experiences. Propel or throttle growth. Business software has become a blocker instead of ways to get work done. There’s another option. Freshworks. With a fresh vision for how the world works.At Freshworks, we build uncomplicated service software that delivers exceptional customer and employee experiences. Our enterprise-grade solutions are powerful, yet easy to use, and quick to deliver results. Our people-first approach to AI eliminates friction, making employees more effective and organizations more productive. Over 72,000 companies, including Bridgestone, New Balance, Nucor, S&P Global, and Sony Music, trust Freshworks’ customer experience (CX) and employee experience (EX) software to fuel customer loyalty and service efficiency. And, over 4,500 Freshworks employees make this possible, all around the world. Fresh vision. Real impact. Come build it with us.Job DescriptionAs a Machine Learning Engineer in the Agentic AI team, you will be at the forefront of the next paradigm shift in artificial intelligence. You will transition from static models to building autonomous, goal-driven intelligent agents capable of dynamic reasoning, tool execution, and complex workflow orchestration. Your role is to bridge the gap between bleeding-edge generative AI research and enterprise-scale, production-ready agentic systems.Impact You Will CreateProductionize Autonomous Systems: Serve as the critical link translating advanced research in LLMs, multi-agent frameworks, and cognitive architectures into highly reliable, product-ready, enterprise-scale implementations.Scale Multi-Agent Workflows: Build and deploy robust ML infrastructures, runtime environments, and data pipelines engineered to orchestrate and execute millions of autonomous agent decisions with low latency, high efficiency, and safety guardrails.Architect Next-Gen AI Frameworks: Drive technical alignment by designing scalable agentic frameworks from the ground up, establishing best practices for prompt engineering frameworks, memory systems, and tool-use integration across the organization.Roles & ResponsibilitiesAgentic 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...