Director, AI/ML Platform Engineering

This role is eligible for our hybrid work model: Two days in-office.

This job posting is for an existing, currently vacant position.

Director, AI/ML Platform Engineering

Our AI/ML Platform team is where intelligent systems get built at scale. We're building the foundational infrastructure that powers machine learning across Priceline—from LLM integrations and agentic workflows to MLOps pipelines and model serving platforms. We enable data scientists and engineers to move from experimentation to production faster, while ensuring governance, observability, and cost efficiency. If you thrive in a fast-paced, innovation-driven environment where platform engineering meets cutting-edge AI, you're in the right place.

Why This Job's a Big Deal

AI and machine learning are transforming how Priceline delivers personalized experiences, optimizes operations, and drives business value. This role leads the engineering vision and execution for Priceline's AI/ML Platform—the unified infrastructure that enables model development, deployment, orchestration, and productionization at scale. From MLOps tooling and feature stores to LLM infrastructure and Model Context Protocol (MCP) integrations, you'll architect the systems that empower teams to build, deploy, and scale AI-driven products safely and efficiently.

In This Role You Will Get To

• Define and execute the technical strategy for Priceline's AI/ML Platform—encompassing MLOps, model serving, feature engineering, experimentation, LLM orchestration, and agentic AI workflows.

• Lead multiple global engineering teams building highly available, scalable ML infrastructure that serves hundreds of models and supports diverse use cases from personalization to fraud detection.

• Architect and evolve MLOps pipelines for the full ML lifecycle: data versioning, feature stores, model training, evaluation, deployment, monitoring, and retraining automation.

• Build and scale LLM infrastructure including prompt management, retrieval-augmented generation (RAG), fine-tuning pipelines, evaluation frameworks, and cost optimization for foundation model workloads.

• Drive adoption of Model Context Protocol (MCP) and agentic frameworks to enable intelligent, context-aware AI applications across Priceline's ecosystem.

• Collaborate closely with data science, ML engineering, product, and platform teams to deliver self-service tools that accelerate model development and reduce time-to-production.

• Champion ML observability, governance, and responsible AI practices—ensuring models are explainable, fair, performant, and compliant with privacy regulations.

• Drive engineering excellence through automation, CI/CD for ML, infrastructure-as-code, and platform reliability engineering—focusing on developer productivity and operational efficiency.

• Mentor, coach, and grow talent within distributed engineering teams, fostering a culture of innovation, experimentation, and technical excellence.

• Engage with leadership and stakeholders across product, engineering, and data teams to align AI/ML platform priorities with Priceline's business objectives and AI strategy.

Who You Are

• 10+ years of experience in software, platform, or ML engineering, with at least 5 years leading teams that build large-scale AI/ML platforms or MLOps infrastructure.

• Proven expertise in MLOps and ML infrastructure: model training orchestration, feature stores, model registries, serving infrastructure, A/B testing frameworks, and monitoring/observability for ML systems.

• Hands-on experience with modern ML platforms and tools: Vertex AI, Kubeflow, MLflow or equivalent; experience with LLM tooling (LangChain, LlamaIndex, vector databases, prompt engineering frameworks).

• Deep understanding of LLM operations: fine-tuning, embeddings, RAG architectures, inference optimization, cost management, and evaluation frameworks for generative AI.

• Familiarity with Model Context Protocol (MCP) or similar standards for enabling AI agents and context-aware applications—or strong ability to drive adoption of emerging AI infrastructure patterns.

• Strong cloud and data platform skills (GCP preferred): BigQuery, Dataflow, Dataproc, GKE, Cloud Run, Pub/Sub, Airflow/Composer, dbt, Spark, Kafka.

• Experience with ML governance: model versioning, lineage tracking, bias detection, explainability, privacy-preserving ML, and compliance (GDPR, CCPA, AI regulations).

• Excellent collaborator who thrives in cross-functional environments and can translate complex ML platform capabilities into business value and product velocity.

• Strategic thinker with the ability to balance long-term platform vision (next-generation AI capabilities) with near-term delivery (enabling teams today).

• Passion for platform engineering: You love building tools and infrastructure that make other engineers 10x more productive.

• Embodies Priceline's core values: Customer, Innovation, Team, Accountability, and Trust—achieving the right results, the right way.

• Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust.

• The Right Results, the Right Way is not just a motto at Priceline; it's a way of life. Unquestionable integrity and ethics is essential.

There are a variety of factors that go into determining a salary range, including but not limited to external market benchmark data, geographic location, and years of experience sought/required. In addition to a competitive base salary, certain roles may be eligible for an annual bonus and/or equity grant.

The salary range for this position is $170,000 to $200,000K CAD

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