ML Ops Manager

Job Description

The Lead MLOps Engineer will be responsible for leading technology initiatives aimed at improving business value andoutcomes in the areas of digital marketing and commercial analytics through the adoption of Artificial Intelligence (AI) enabled solutions. Working with cross-functional teams across AI projects to operationalize data science models to deployed scalable solutions delivering business value. They should be inquisitive and bring an innovate mindset to work every day, researching, proposing, and implementing MLOps process improvements, solution ideas and ways of working to be more agile, lean and productive. Provide leadership and technical expertise in operationalizing machine learning models, bridging the gap between data science and IT operations. Key responsibilities include designing, implementing, and optimizing MLOps infrastructure, building CI/CD pipelines for ML models, and ensuring the security and scalability of ML systems. Key Responsibilities Architect & Deploy: Design and manage scalable ML infrastructure on Azure (AKS), leveraging Infrastructure as Code principles. Automate & Accelerate: Build and optimize CI/CD pipelines with GitHub Actions for seamless software, data, andmodel delivery. Engineer Performance: Develop efficient and reliable data pipelines using Python and distributed computing frameworks. Ensure Reliability: Implement solutions for deploying and maintaining ML models in production. Collaborate & Innovate: Partner with data scientists and engineers to continuously enhance existing MLOps capabilities. Key Competencies: Experience: A minimum of 5+ years of experience in software engineering, data science, or a related field with experience in MLOps is typically required. Education: A bachelor's or master's degree in Computer Science / Engineering. Soft Skills: Strong analytical and problem-solving skills, excellent communication and collaboration skills, and the ability to work in a fast-paced environment are highly valued. Azure & AKS: Deep hands-on experience. IaC & CI/CD: Mastery of Terraform/Bicep & GitHub Actions. Data Engineering: Advanced Python & Spark for complex pipelines. ML Operations: Proven ability in model serving & monitoring. Problem Solver: Adept at navigating complex technical challenges and delivering solutions.