Project Manager, MLOps

About the Role

In this role, you will be instrumental in developing scalable MLOps solutions that underpin applications and middleware/cloud systems within our Software Defined Factory (SDF). This position offers a unique chance to work on cutting-edge MLOps practices tailored for manufacturing, enhancing existing workflows and delivering innovative AI-driven MLOps features for production-scale deployments.

Key Responsibilities

• Formulate and implement a roadmap for transforming MLOps edge and cloud infrastructure.
• Coordinate efforts across multiple teams, track project milestones, monitor risks, and communicate progress effectively.
• Oversee AI project management including planning, execution, and monitoring to ensure timely delivery of goals.
• Advocate and apply Agile, Scrum, or hybrid project management methodologies as suited to project needs.
• Lead mid-sized migration initiatives ensuring smooth transitions with emphasis on performance, security, and cost-effectiveness.
• Design and execute strategies for data preparation and placement to maximize data usability.
• Guide data scientists in strategizing future AI and MLOps solutions, focusing on AI/MLOps Hub, model metrics, and ensuring data quality.
• Develop and refine best practices, tools, and frameworks for MLOps to boost developer productivity and system agility.
• Drive continuous enhancements in system scalability, optimization, and comprehensive testing for MLOps platforms.
• Manage vendor relations, negotiate contracts, and ensure efficient resource utilization.
• Stay up-to-date with industry trends to incorporate state-of-the-art technologies improving developer reliability and efficiency.
• Lead enterprise-level edge and cloud MLOps transformations for machine learning models and pipelines on a regional and global scale.
• Establish infrastructure, deployment, and operational standards within the IoT platform environment.
• Provide leadership and mentorship to team members, fostering career development and performance excellence.
• Communicate regularly with stakeholders and teams, delivering performance feedback and evaluations.
• Build strong partnerships with Software Architects, Technical Leads, Product Owners, Data Scientists, Engineers, and Developers.
• Prepare detailed reports on project progress, challenges, and successes to senior management and stakeholders.
• Collaborate with IoT/PLC engineers and domain experts effectively.
• Document design decisions, technical standards, and maintain thorough playbooks.
• Mentor junior engineers and offer training in AI and ML technologies.
• Develop training resources and conduct workshops to enhance cross-functional team skills.

Candidate Profile

• Possess a bachelor's degree in Computer Science, Computer Engineering, or a related machine learning technical discipline.
• Have over 6 years experience in MLOps, AI, Cloud, DevOps, with solid exposure to manufacturing plant environments.
• Demonstrate at least 4 years building and deploying machine learning applications at scale, globally or regionally.
• Bring at least 2 years of experience managing software and ML agile teams.
• Experienced in AI/ML project coordination, including prompt engineering, AI/ML workflows, data handling, model training, and testing (NLP, LLM).
• Proven advisor experience in AI/MLOps consulting with team coordination duties.
• Capable of leading data scientist teams and producing in-depth documentation.
• Expertise with CI/CD pipeline development, MLOps tools and practices, cloud infrastructure architectures, and launching complex ML applications in cloud environments.
• Familiar with PostgreSQL, InfluxDB, and migrating legacy databases to modern cloud-native setups.
• Proficient in software development best practices including Git, version control, automated testing, and peer code reviews.
• Experience in monitoring application performance, optimizing for scalability and efficiency.
• Hands-on experience with Kubernetes, OpenShift, containers, and cloud-native platforms.
• Strong understanding of AWS cloud services and hybrid/multi-cloud deployment strategies supporting automated data and CI/CD pipelines.
• Excellent leadership, communication, and stakeholder engagement skills.
• Certified Kubernetes Administrator (CKA) qualification.
• Possession of AWS cloud certifications and CI/CD DevOps or related ITIL and TOGAF certifications.
• Exposure to PTC Kepware and Thingworx platforms integrated with GitLab/GitHub for CI/CD pipelines and global releases.
• Practical knowledge of OpenStack VM, VMware, Red Hat OS virtualization, and CaaS infrastructure management.
• Experienced in building and operating CaaS environments, including container deployment and scaling via APIs, virtualization systems, or portals.