Principal AI Engineer

Skills

About the Role

You will lead the design, development, and delivery of intelligent software systems that bring together enterprise application architecture, full-stack engineering, data science, and machine learning. In this highly visible, hands-on leadership role, you will build and scale production-grade platforms and AI-enabled products that power critical workflows, decision-making, automation, and business insight across the organization. You will combine deep software engineering expertise with strong applied AI and data science capability, mentor engineers, shape technical direction, and partner cross-functionally to turn complex business problems into scalable, secure, and reliable solutions.

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience; advanced degree is a plus
• 10+ years of experience in software engineering, applied machine learning, data science, or related fields, including building and delivering production systems end-to-end
• Strong hands-on expertise in modern software engineering, including backend development, APIs, and scalable system design
• Experience with full-stack development, including modern frontend frameworks and service-based architectures
• Proficiency in Python and SQL, with experience in data manipulation, model development, and analytical workflows
• Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar
• Strong experience with cloud platforms (AWS, Azure, or GCP) and building cloud-native systems
• Experience with modern data platforms such as Snowflake, BigQuery, or Databricks
• Familiarity with Infrastructure as Code, microservices, and event-driven architectures
• Strong grounding in statistics, experimentation, and analytical methods
• Experience deploying and maintaining production AI/ML systems, including monitoring and governance
• Solid understanding of secure system design, data privacy, and enterprise engineering practices
• Excellent communication skills with the ability to influence stakeholders at all levels
• Proven experience mentoring engineers and leading through influence in a hands-on environment
• Experience in media, entertainment, or similarly fast-paced industries is a plus

Responsibilities

• Lead the design, development, and deployment of machine learning, statistical, and AI-driven solutions that support automation, prediction, decision-making, and personalization
• Own end-to-end delivery of AI-enabled products and services, from problem framing and data exploration through model development, application integration, deployment, and monitoring
• Evaluate and implement appropriate approaches across machine learning, analytics, and natural language processing based on business needs and technical constraints
• Partner with data and platform teams to build robust pipelines, reusable services, and scalable environments that support production AI workloads
• Design and develop scalable, secure, cloud-native applications and backend services that integrate AI, data, and business workflows into enterprise platforms
• Lead end-to-end full-stack engineering across backend services and modern web applications, including APIs, data models, service integrations, and internal tools
• Architect systems using modern cloud patterns such as microservices, event-driven design, and managed services, ensuring reliability, observability, and scalability
• Provide architectural leadership across integrations with enterprise systems and third-party platforms
• Productionize AI and machine learning solutions using modern ML Ops and software engineering practices
• Establish standards for testing, deployment, observability, drift detection, retraining, and documentation
• Drive quality, automation, and performance in systems where accuracy, resilience, and reliability are critical
• Serve as a hands-on technical leader and player-coach, mentoring engineers while actively contributing to design and implementation
• Help define technical strategy and roadmap priorities for AI, data, and application development
• Lead execution of complex, cross-functional initiatives and act as a senior escalation point for technical decisions and trade-offs
• Foster a culture of engineering excellence, accountability, and continuous improvement
• Partner with Product, Engineering, Data, and business stakeholders to identify high-value opportunities where AI and software can materially improve outcomes
• Translate ambiguous business needs into clear technical solutions, communicating trade-offs and risks effectively
• Present complex technical work in a clear, actionable way to both technical and non-technical audiences, including leadership