• Support data science teams in building, deploying, and operating machine learning solutions at scale • Model Deployment: Ensuring that machine learning models are deployed efficiently and reliably • Model Monitoring: Continuously monitoring the performance of models to detect issues • Automation: Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation • Collaboration: Working closely with data scientists, software engineers, and IT operations • Version Control and Governance: Managing version control for models and ensuring compliance with governance policies • Optimization: Identifying and implementing ways to improve the performance and scalability of ML systems

• Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. • Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters. • Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows). • Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost). • Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models. • Collaborate with researchers to productionize models and accelerate training/inference pipelines. • Establish ML Ops best practices, internal standards, and cross-team tooling. • Mentor engineers and influence architectural direction across the entire AI platform.

• Design, build, and release AI products/features that solve real user problems • Design and implement streaming/batch data pipelines to support training and inference • Write production-ready code and move it to production with the help of cloud services and CI/CD techniques • Continuously monitor and improve the quality and performance of the systems • Learn by doing — while owning real problems and demonstrating end-to-end ownership throughout the system • Build resilient, performant pipelines and services in production • Own system architecture, performance, observability, and scalability • Collaborate across teams to turn ideas into impactful products • Mentor engineers and help set technical direction

• Design, build, and release AI products/features that solve real user problems • Design and implement streaming/batch data pipelines to support training and inference • Write production-ready code and move it to production with the help of cloud services and CI/CD techniques • Continuously monitor and improve the quality and performance of the systems • Learn by doing—while owning real problems and demonstrating end-to-end ownership through the whole system