AI Engineering Technical Lead

Paramount•Burbank, CA

5d•$156,800 - $235,200

About The Position

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 across Paramount 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.

Requirements

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.
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.
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.
Strong experience with cloud-native architectures (GCP preferred).
Experience deploying ML systems in Kubernetes-based environments.
Understanding of distributed systems, scalability, and fault tolerance.
Proficiency in Python, Java, or similar languages for production systems.
Experience building microservices and APIs for model serving.
Strong software engineering fundamentals, including testing, CI/CD, and observability.
Strong foundation in machine learning engineering, data systems, and distributed architecture.
Proven track record of building and scaling AI/ML systems in production environments.
Experience working with real-time data platforms and high-scale user-facing systems.
Ability to balance long-term architecture with rapid product delivery.
Excellent leadership, problem-solving, and cross-functional collaboration skills.
Self-motivated, quality-driven, and focused on delivering measurable impact through AI.

Nice To Haves

Experience in streaming, media, or consumer-facing platforms.
Familiarity with experimentation platforms (A/B testing) and metrics-driven optimization.
Experience building AI-powered data products and platform capabilities

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.