Job Description
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.
Prototyping & POC Execution: Lead Proof of Concept (POC) initiatives across diverse tech stacks to identify and validate optimal infrastructure solutions for complex business challenges.
Lifecycle Ownership: Independently own the full lifecycle of feature delivery, from initial requirement gathering with product teams to final deployment and monitoring.
Qualifications
Skills
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.
Qualifications
Experience: 6–9 years of professional experience in software engineering and machine learning development.
Track Record: A proven history of successfully building, productionizing, and maintaining Machine Learning solutions at scale.
Education: Degree in Computer Science, Artificial Intelligence, or a related quantitative field.
Additional Information
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.
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