Senior ML Platform Engineer

• Design and implement our greenfield ML platform and suite of analytical AI agents, 10Xing the Data Science team’s impact.

• Work closely with Data Scientists to understand their development needs
• Work closely with Stakeholders to understand the business problems driving model deployments, translating complex models into self-serve, LLM-callable tools that augment stakeholder workflows.
• Assess and integrate cutting-edge software and practices in Data Science and MLOps.
• Refactor disjointed data science scripts and workflows into modular ‘skills’ that act as procedural knowledge for AI agents, streamlining stakeholder interactions.

Key Projects:

• Manage deployment and contribute to models driving Paramount+’s global, financial forecasts
• Design and implement Paramount’s model deployment platform
• Design and implement robust monitoring, logging, alerting, and observability of ML deployments
• Design and implement UIs and natural-language AI agents that enable a variety of stakeholders to intuitively interact with DS models.
• Drive adoption of best practices for CI/CD workflows tailored to Data Science & Agentic Workflows
• Drive adoption of agentic workflows (e.g., automated SQL generation, variance diagnostics, model post-mortems) across Data Science and the larger Paramount organization.

We are seeking a Sr. ML Platform Engineer who is excited to deploy MLOps products that shape business strategy, optimize content, inform marketing investment decisions, and enhance the user experience. You will build the foundations of our ML platform that will enable the Data Science team to deploy models at web scale data driving interactions with millions of customers across the globe. Specific projects will include designing the ML Ops platform and Agentic AI layer that will enable the Data Science team to scale their model deployments and automate analytical workflows. Success in this role requires a strong foundation in Object-Oriented Programming (OOP), comfort writing production-quality code in a collaborative environment, effective communication with Data Science team and stakeholders, and a team player who is excited to design and develop the MLOps deployment that will enable the team to multiply their impact., You Have:

• 5+ years experience in Data Science and ML Engineering.
• Deep experience developing as a team using Object-Oriented Programming (OOP), Docker, & Kubernetes
• Deep experience designing and implementing MLOps platforms and multi-agent systems in a cloud environment.
• Deep experience implementing CI/CD workflows that manage model deployments
• Experience developing containerized applications
• Ability to innovate without over-engineering
• Familiarity with well-known statistical and ML models and methods
• Strong detail orientation with a penchant for deployment reliability
• Must successfully pass a background check

You might also have:

• Experience integrating LLMs into existing business processes and creating tools using the Model Context Protocol (MCP).
• Experience developing APIs
• Experience working at Web scale
• Experience using Google Cloud Platform (BigQuery, ML Engine, and APIs).