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.