Your Mission

This position will be responsible for building scalable MLOps as a technical foundation for applications and our middleware/cloud towards our Software Defined Factory (SDF). This is a great opportunity to apply your talent in working on leading-edge MLOps for manufacturing, building state-of-the-art MLOps features and improving existing practices across various tools, applications and platforms. You will drive innovation in AI-driven MLOps designed for production-scale deployment.

What To Expect

[Project Leadership]

Define and execute the roadmap for MLOps edge and cloud infrastructure transformation.
Coordinate cross-functional teams, monitor project performance, and report progress including risks and mitigations.
Manage overall AI projects (planning, execution, and monitoring), including tracking project milestones and deliverables toward timely completion.
Champion Agile, Scrum, or hybrid methodologies as appropriate.

[Technical AI and MLOps]

Lead medium-scale migrations, ensuring seamless transitions with minimal disruption while optimizing performance, security, and cost.
Implement and facilitate data preparation and placement strategies to optimize data usability.
Lead a team of data scientists on future AI and MLOps strategies, including focus on an AI/MLOps Hub for agentic agents, model metrics, and data integrity.
Establish and refine MLOps best practices, tools, and frameworks to enhance developer experience, agility, and efficiency.
Drive continuous improvements in MLOps edge/cloud performance, scalability, optimization, and robust system testing delivery.
Manage relationships with key technology vendors, negotiate contracts, and ensure optimal utilization and cost efficiency.
Review industry trends with continuous evaluation of cutting-edge technologies to improve developer reliability and efficiency.
Demonstrate experience in leading enterprise-scale edge and MLOps transformations (regionally/globally) for ML models and pipelines to the cloud.
Develop and enforce best practices for infrastructure, application deployment, and operations within the IoT Platform.
Develop your team by coaching, facilitating career progression, and providing growth opportunities.
Regularly communicate with team members, provide performance feedback, and conduct evaluations.
Build collaborative partnerships with Software Architects, Technical Leads, Product Owners, Product Managers, Data Scientists, Data Engineers, and Software Developers.

[Technical Leadership & Collaboration]

Prepare reports on project progress, challenges, and achievements.
Collaborate closely with IoT/PLC engineers and domain experts.
Present findings to stakeholders and senior management.
Contribute to and maintain comprehensive documentation, design decisions, and technical standards via playbooks.
Mentor junior engineers through training and support team members on AI and ML tools and techniques.
Develop training materials and conduct workshops as needed to enhance cross-functional team capabilities.

What You'll Bring

Bachelor's degree in Computer Science, Computer Engineering, or another machine learning-related technical field.
6+ years of experience in MLOps, AI, Cloud, DevOps, and related domains, with a strong background in manufacturing plant environments.
4+ years of experience building and deploying successful machine learning software applications at scale (globally/regionally).
2+ years of direct people management experience leading software and ML agile teams.
Experience as an AI/ML Project Coordinator with familiarity in prompt engineering, AI/ML workflows, data handling, and model training and testing (NLP, LLM, etc.).
Experience as an AI/MLOps consultant with team coordination responsibilities.
Ability to lead a team of Data Scientists and create comprehensive documentation.
Deep experience with CI/CD pipelines, MLOps practices and tools, cloud infrastructure architecture, and launching complex ML applications in cloud environments.
Experience with PostgreSQL, InfluxDB, and other databases, including migration and modernization from legacy systems to cloud-native environments.
Strong background in software development, Git version control, test automation, and code reviews.
Experience in performance monitoring and optimization to ensure scalability and efficiency.
Hands-on experience with Kubernetes, OpenShift, containers, and cloud-native architectures.
Strong knowledge of cloud platforms (AWS) and hybrid/multi-cloud strategies for launching automated data and CI/CD pipelines.
Excellent leadership, communication, and stakeholder management skills.
Certified Kubernetes Administrator (CKA) certification.
AWS cloud certifications.
CI/CD DevOps certifications or other ITIL and TOGAF certifications.
Experience with PTC Kepware and Thingworx, integrated into GitLab/GitHub as a CI/CD pipeline with global release experience.
Hands-on expertise with OpenStack VM, VMware, Red Hat OSV, and CaaS infrastructure.
Experience creating and managing CaaS environments, including deploying and scaling containers and applications through APIs, virtualization, or portals.

Project Manager, MLOps