Lead Software Engineer - Machine Learning

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Skills you'll use

What you'll do

• Impact You Will Create
• Bridge Research and Production: Serve as the critical link translating theoretical data science research and sophisticated algorithms into product-ready, enterprise-scale implementations.
• Scale to Millions: Build and deploy robust ML APIs and data pipelines engineered to handle millions of requests with high efficiency, low latency, and reliability.
• Architect from Scratch: Drive organizational technical alignment by architecting high-performance ML solutions from the ground up and leading cross-functional adoption.
• Roles & Responsibilities
• ML Algorithm Implementation: Collaborate with Data Scientists to translate complex models and experimental algorithms into clean, high-performance, production-grade code.
• End-to-End Pipeline Architecture: Design, build, and manage comprehensive ML pipelines encompassing data pre-processing, model generation, automated deployment, cross-validation, and active feedback loops.
• High-Performance Service Delivery: Develop and deploy extensible, scalable ML API services optimized for minimal latency under high traffic loads.
• Operational Intelligence: Design and implement monitoring systems to track engineering efficiency and active ML model performance metrics, ensuring long-term system health.
• Strategic Innovation & Collaboration: Architect technical solutions from scratch and liaise with cross-product architects and engineers to ensure organizational alignment.

Who can apply

• Production ML Engineering: Proven capability in translating advanced mathematical models into optimized, production-ready software.
• MLOps Mastery: Deep expertise in lifecycle management practices, ensuring seamless model transitions from experimental stages to live production environments.
• Distributed Pipeline & API Design: Strong architecture skills in building scalable data pipelines and low-latency API microservices.
• System Telemetry & Monitoring: Proficiency in establishing monitoring frameworks for tracking engineering efficiency, system health, and model performance metrics.
• Technical Project Leadership: Ability to execute rapid prototyping, evaluate technical stacks, and lead cross-functional technical alignment.

About Freshworks

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