AI Engineering Technical Lead

Paramount

AI Engineering Technical Lead developing intelligent systems for Paramount streaming platforms. Leading architectural decisions and mentoring engineers on AI/ML projects.

Posted 7/18/2026full-timeRemote • New York • 🇺🇸 United StatesSenior💰 $156,800 - $235,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models in production, with a strong focus on real-time inference systems and scalable ML infrastructure. Proficient in integrating AI capabilities with data platforms and ensuring model performance and reliability.

Highest-signal resume keywords

Machine Learning Model DeploymentFeature Engineering PipelinesReal-Time Data IntegrationCloud-Native ArchitecturesAPI Development for AI Services

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringModel Training FrameworksData Modeling and TransformationDistributed SystemsPython ProgrammingJava ProgrammingKubernetesFeature StoresObservability PracticesEvent-Driven Architectures

Soft Skills

LeadershipProblem-SolvingCross-Functional CollaborationSelf-MotivationQuality-Driven Focus

Tools & Technologies

TensorFlowPyTorchKafkaPub/SubCI/CD ToolsMicroservices ArchitectureReal-Time Data PlatformsModel RegistriesInference ServicesStreaming Data Platforms

Industry Keywords

AI Systems DesignReal-Time InferenceBatch ProcessingScalable ML InfrastructureProduction Reliability

Tech Stack

Tools & technologies

CloudDistributed SystemsGoogle Cloud PlatformJavaKafkaKubernetesMicroservicesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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