AI Engineering Technical Lead

• 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.

• 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., Tensor Flow, 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.

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

• Machine Learning Model Deployment
• Real-Time Inference Systems
• Feature Engineering Pipelines
• Cloud-Native Architectures
• Cross-Functional Collaboration

• Machine Learning Engineering
• Feature Engineering
• Model Evaluation
• Data Modeling
• Python
• Java
• Tensor Flow
• Py Torch
• Kubernetes
• APIs

• Leadership
• Problem-Solving
• Collaboration
• Self-Motivated
• Quality-Driven

• AI Systems
• Real-Time Data Platforms
• Scalable ML Infrastructure
• Event-Driven Architectures
• Distributed Systems

• Kafka
• Pub/Sub
• CI/CD
• Observability
• Microservices