Dice is the leading career destination for tech experts at every stage of their careers. Our client, Aptino, is seeking the following. Apply via Dice today! **Job Summary** The ML Ops Lead is responsible for architecting, implementing, and operating scalable and reliable machine learning infrastructure and workflows that take AI/ML models from experimentation into robust production environments. This role balances *hands-on engineering excellence* with *team leadership* and *strategic ownership* of machine learning operations practices across the organization. **Key Responsibilities** - ML Infrastructure & Operations - Architect and maintain scalable ML infrastructure, including compute, storage, orchestration, and monitoring, in cloud (AWS, Azure, Google Cloud Platform) and/or hybrid environments. - Build and manage end-to-end ML pipelines for data ingestion, model training, validation, deployment, monitoring, and retraining. - Containerize and orchestrate workloads using Docker, Kubernetes (EKS/AKS/GKE), Terraform, or similar IaC tools. - CI/CD & Automation - Design and operate CI/CD workflows for ML workflows (model retraining, version control, deployment, rollback). - Automate testing, validation, and release processes for production ML systems. - Production Reliability & Monitoring - Establish monitoring, logging, observability, drift detection, and alerting for deployed models. - Troubleshoot operational issues, optimize performance, and ensure high availability and scalability. - Leadership & Strategic Ownership - Lead, mentor, and grow a team of MLOps engineers & platform specialists. - Drive ML Ops strategy and roadmap, aligned with business goals and regulatory standards. - Collaborate closely with data scientists, software engineers, product owners, and DevOps teams to deliver production-ready models. - Governance & Best Practices - Implement governance, security, auditability, and compliance practices across ML operations. - Define and promote ML lifecycle best practices, documentation standards, and performance metrics. **Skills & Qualifications** **Technical Expertise** - Strong experience with ML Ops tools/frameworks: Kubeflow, MLflow, Airflow, TensorFlow Serving, TorchServe, Sagemaker, Azure ML, etc. - Proficiency in cloud platforms (AWS, Google Cloud Platform, Azure) and orchestration technologies (Docker & Kubernetes). - Solid background in Infrastructure as Code (Terraform, CloudFormation, Bicep, CDK). - Deep understanding of CI/CD pipelines, automation tooling, and version control systems. - Monitoring and observability tooling experience (Prometheus, Grafana, Azure Monitor, etc.). **Soft & Leadership Skills** - Demonstrated ability to lead technical teams and mentor engineering talent. - Excellent communication and cross-functional collaboration skills. - Strategic mindset with a focus on reliability, scalability, and operational excellence. **Education & Experience** - Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or related field. - Typically 7+ years of experience in cloud/DevOps/ML Ops related roles; senior experience preferred depending on scale of operations.