AI Engineering Technical Lead

🔥 0 minutes ago

🗽 New York – Remote

🗽 New York – Remote

💵 $156.8k - $235.2k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

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Paramount

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1912

📱 Media

👥 B2C

Media • B2C • Entertainment

Paramount is a global multimedia entertainment and news company that offers a range of services including direct-to-consumer digital subscription video on-demand and live streaming through Paramount+. It also owns Pluto TV, a leading free streaming television service, MTV, the world’s premier youth entertainment brand, and CBS Sports, a leader in television sports broadcasts. Paramount Pictures, since 1912, has been a legendary producer and distributor of films, hosting a library of over 1,000 titles. The company is deeply committed to inclusion and impact, focusing on diversity, global sustainability, and content that affects change. Being a significant player in both live and on-demand streaming services, Paramount embraces a wide array of content from sports to kids’ entertainment, comedy, and groundbreaking documentaries, impacting both linear and streaming platforms globally.

📋 Description

• 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.

🎯 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.

🏖️ Benefits

• medical • dental • vision • 401(k) plan • life insurance coverage • disability benefits • tuition assistance program • PTO