AI Engineering Technical Lead

#WeAreParamount on a mission to unleash the power of content… you in?
We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

AI Engineering Technical Lead

The Applied Intelligence Data Engineering team is seeking an AI Engineering Technical Lead
to drive the design, development, and productionization of AI-powered data products acrossParamount streaming platforms.

This role will lead the development of intelligent systems that leverage real-time and batch data
to power personalization, recommendations, content discovery, and advanced analytics. You will
work at the intersection of data engineering, machine learning, and software engineering,
building scalable AI systems that integrate tightly with our real-time data ingestion platform.
As a technical leader, you will define architecture, guide implementation, mentor engineers, and
ensure production-grade performance, scalability, and reliability of AI-driven systems.

Key Responsibilities
Lead AI System Design & Development
● Develop real-time and batch inference pipelines integrated with streaming data
platforms.
● Design feature engineering pipelines leveraging high-volume behavioral and content
metadata.
● Implement end-to-end ML workflows from data ingestion to model serving.
Build AI-Powered Data Products
● Develop production-grade AI services that power user-facing and internal data products.
● Design APIs and services to expose AI capabilities to downstream applications and platforms.
● Ensure tight integration between AI systems and the core data platform.
Architect Scalable ML Infrastructure
● Define architecture for model training, evaluation, deployment, and monitoring.
● Build and optimize feature stores, model registries, and inference services.
● Design systems that support low-latency, high-throughput model serving.
● Establish best practices for reproducibility, versioning, and lifecycle management.
Production Reliability & Model Performance
● Monitor and optimize model performance, latency, and system reliability in production.
● Implement observability for data quality, feature drift, and model degradation.
● Establish automated testing, validation, and deployment pipelines for ML systems.
● Ensure scalability and cost efficiency across AI workloads.
Cross-Functional Collaboration
● Partner with Data Engineers to integrate AI pipelines with real-time and batch data systems.
● Collaborate with Product Managers to define AI-driven product capabilities and roadmap.
● Work with Software Engineers to integrate AI services into user-facing applications.
● Align with analytics and experimentation teams to measure model impact.
Technical Leadership
● Lead architectural decisions for AI/ML systems and data-driven applications.
● Mentor engineers in machine learning engineering, system design, and best practices.
● Establish standards for model development, deployment, and operational excellence.
● Drive innovation in applied AI across streaming and content platforms.

Required Technical Skills
Machine Learning & AI Systems
● Strong experience building and deploying machine learning models in production.
● Expertise in recommendation systems, personalization, ranking models, or NLP.
● Experience with model training frameworks (e.g., TensorFlow, PyTorch, or similar).
● Understanding of feature engineering, model evaluation, and experimentation frameworks.
Data & Feature Engineering
● Experience designing large-scale feature pipelines using batch and streaming data.
● Strong knowledge of data modeling and transformation for ML use cases.
● Familiarity with feature stores and real-time feature serving architectures.
Streaming & Real-Time Systems
● Experience integrating ML systems with real-time data platforms (e.g., Kafka, Pub/Sub).
● Understanding of event-driven architectures and low-latency processing patterns.
● Ability to design real-time inference and decisioning systems.
Cloud & Distributed Systems
● Strong experience with cloud-native architectures (GCP preferred).
● Experience deploying ML systems in Kubernetes-based environments.
● Understandin