Lead Software Engineer - Machine Learning

Requirements

• 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.
• 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.

Benefits

Additional Information

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.

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Company Intel

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