AI Engineering Technical Lead

This is a fully remote position, open to applicants in New York.

📋 Description

• Lead the design and development of AI systems.

• Create real-time and batch inference pipelines that integrate with streaming data platforms.

• Engineer feature pipelines using high-volume behavioral and content metadata.

• Implement comprehensive ML workflows from data ingestion to model deployment.

• Build AI-driven data products.

• Develop production-ready AI services that enhance both user-facing and internal data products.

• Design APIs and services to provide AI functionalities to downstream applications and platforms.

• Ensure seamless integration between AI systems and the core data platform.

• Architect scalable ML infrastructure.

• Define the architecture for model training, evaluation, deployment, and monitoring.

• Build and optimize feature stores, model registries, and inference services.

• Create systems that support low-latency and high-throughput model serving.

• Establish best practices for reproducibility, versioning, and lifecycle management.

• Ensure production reliability and model performance.

• Monitor and enhance model performance, latency, and system reliability in production settings.

• Implement observability mechanisms for data quality, feature drift, and model degradation.

• Set up automated testing, validation, and deployment pipelines for ML systems.

• Ensure scalability and cost-effectiveness across AI workloads.

• Foster cross-functional collaboration.

• Collaborate with Data Engineers to blend AI pipelines with real-time and batch data systems.

• Work with Product Managers to outline AI-driven product capabilities and roadmaps.

• Partner with Software Engineers to integrate AI services into user-facing applications.

• Align with analytics and experimentation teams to assess model impact.

• Provide technical leadership.

• Guide architectural decisions for AI/ML systems and data-centric applications.

• Mentor engineers in machine learning engineering, system design, and best practices.

• Set standards for model development, deployment, and operational excellence.

• Promote innovation in applied AI across streaming and content platforms.

⛳️ Requirements

• Extensive experience in building and deploying machine learning models in production environments.

• Proficiency in recommendation systems, personalization, ranking models, or natural language processing (NLP).

• Familiarity with model training frameworks such as TensorFlow, PyTorch, or similar tools.

• Understanding of feature engineering, model evaluation, and experimentation frameworks.

• Experience in designing large-scale feature pipelines utilizing both batch and streaming data.

• Strong knowledge of data modeling and transformation for machine learning applications.

• Familiarity with feature stores and architectures for real-time feature serving.

• Experience integrating machine learning systems with real-time data platforms like Kafka or Pub/Sub.

• Understanding of event-driven architectures and low-latency processing methodologies.

• Capability to design real-time inference and decision-making systems.

• Significant experience with cloud-native architectures, preferably Google Cloud Platform (GCP).

• Proficient in deploying ML systems in Kubernetes environments.

• Understanding of distributed systems, scalability, and fault tolerance principles.

• Proficiency in programming languages such as Python, Java, or similar for production environments.

• Experience developing microservices and APIs for model serving.

• Solid software engineering fundamentals, including testing, continuous integration/continuous deployment (CI/CD), and observability.

• Strong foundation in machine learning engineering, data systems, and distributed architecture.

• Proven success in building and scaling AI/ML systems in production settings.

• Experience with real-time data platforms and large-scale user-facing systems.

• Ability to balance long-term architectural goals with rapid product delivery.

• Excellent leadership, problem-solving, and cross-functional collaboration abilities.

• Self-driven, quality-oriented, focused on delivering measurable impacts through AI.

🏝️ Benefits

• Medical coverage

• Dental coverage

• Vision coverage

• 401(k) retirement plan

• Life insurance coverage

• Disability benefits

• Tuition assistance program

• Paid time off (PTO)