AI Architect

At Anaplan, we are a team of innovators focused on optimizing business decision‑making through our leading AI‑infused scenario planning and analysis platform so our customers can outpace their competition and the market.

What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.

Our customers rank among the who’s who in the Fortune 50. Coca-Cola, Linked In, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best‑in‑class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small.

Supported by operating principles of being strategy‑led, values‑based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!

You will join the Predictive Intelligence engineering team within Anaplan, the group responsible for the Syrup platform and the family of forecasting and predictive services that power Anaplan’s verticalized solutions. The team builds and operates the ML platform, forecasting engines, and data processing services that deliver demand forecasting, inventory recommendations, and predictive insights to enterprise customers in retail, supply chain, and beyond.

This role reports to the Director of Engineering for Predictive Intelligence and serves as the senior technical leader across the predictive intelligence pillar.

• Own the overall architecture and technical strategy for Anaplan’s predictive intelligence services across multiple verticalised solutions.
• Drive a unified architectural approach across disparate intelligence services, reducing duplication and establishing shared platform primitives for forecasting, model serving, and data processing.
• Provide hands‑on design and implementation leadership for major features and platform initiatives, including writing and reviewing critical‑path code where the work demands it.
• Define and oversee the MLOps strategy spanning model training, deployment, monitoring, and lifecycle management on Kubernetes across AWS, GCP, and Azure.
• Establish and evangelize engineering best practices across services, including standards for scalability, fault tolerance, observability, and operational excellence.
• Mentor senior engineers and partner closely with data scientists to elevate technical quality and shorten the path from research to production.
• Partner with Product, Data Science, and Platform leadership to shape the multi‑quarter technical roadmap and influence cross‑organisation decisions on predictive intelligence.
• Represent the AI engineering organisation in technical conversations and architectural reviews with outside teams.

• Strong experience working within Software Engineering, Principal or Architect‑level role owning systems at scale.
• Deep expertise in Python and in designing and operating high‑availability, production ML services.
• Hands‑on experience designing and operating data and analytics platforms built on technologies such as Snowflake, Iceberg, Trino, and Postgres.
• Strong production experience with Kubernetes and at least one major cloud provider (AWS, GCP, or Azure), with working familiarity across multi‑cloud environments.
• Demonstrated experience with MLOps tooling and patterns, including model registries, experiment tracking, and CI/CD for.
• Proven ability to lead architectural strategy across multiple teams and services, balancing long‑term vision with pragmatic delivery.
• Strong communicator who can translate complex technical decisions into clear narratives for engineers, data scientists, product partners, and executives.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

• Experience operating forecasting models in production, including gradient‑boosted methods, neural networks, and constraint‑based optimization solvers.
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