Position Overview
The company, a global leader in *3D* design, engineering, manufacturing, and entertainment software, is seeking a skilled MLOps Engineer to join our AI/ML Platform team. This role is pivotal in ensuring the smooth operationalization of *machine learning* models and the overall efficiency of our next‑generation AI/ML platform used in the development of *machine learning* and generative AI solutions powering the company’s suite of products and services. You will collaborate with research and product engineering from various domains including design, construction, manufacturing, and media & entertainment to support platform operations.
Responsibilities
- Operational Excellence: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices.
- Deployment Automation: Design and implement automated deployment pipelines for *machine learning* models, ensuring seamless transitions from development to production.
- Scalable Infrastructure: Collaborate with cross‑functional teams to design, implement, and maintain scalable infrastructure for *model* training, inference, and data processing.
- Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track *model* performance, system health, and overall platform efficiency.
- Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for *model* training and validation.
- Version Control and *Model* Governance: Implement version control systems for *machine learning* models and contribute to *model* governance practices.
- Governance and Trust: Contribute to the implementation of robust *model* governance practices, version control systems, and adherence to compliance standards. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions.
- Security and Compliance: Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security.
- Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the overall MLOps lifecycle.
- Troubleshooting and Incident Response: Play a key role in identifying and resolving operational issues, contributing to incident response and system recovery.
Minimum Qualifications
- Educational Background: BS or MS in Computer Science, or related field.
- MLOps Experience: 3+ years of hands‑on experience in DevOps and MLOps, with a focus on deploying and managing *machine learning* models in production environments.
- Infrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform or Ansible.
- Containerization: Strong expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling *machine learning* workloads.
- CI/CD: Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment pipelines for *machine learning* projects.
- Scripting and Automation: Strong scripting skills in Python, Bash, or similar languages for automating operational processes.
- Monitoring Tools: Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and *model* performance.
- Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards.
- Collaboration Skills: Excellent collaboration and communication skills, working effectively with cross‑functional teams including data engineers, software developers, and researchers.
- Problem‑solving Skills: Proven ability to troubleshoot and resolve complex operational issues in a timely manner.
Preferred Qualifications
- Cloud Experience: Experience with cloud platforms, especially AWS or Azure, for deploying and managing *machine learning* infrastructure.
- Database Knowledge: Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes.
- *Machine Learning* Frameworks: Exposure to popular *machine learning* frameworks (*TensorFlow*, *PyTorch*) and their integration into MLOps processes.
- Collaboration Tools: Previous experience with collaboration tools like Git for version control and Jira for project management.
- Agile Methodology: Familiarity with Agile development methodologies and working in an iterative, collaborative environment.
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