Applied AI Engineer, Learning Intelligence

Responsibilities

• Own the architecture and technical strategy for learning platforms and enablement products from discovery through production operation and evolution.
• Design and implement AI-powered capabilities, including recommendation systems, skill inference, intelligent search, personalization, and analytics-driven learning workflows.
• Build secure, privacy-aware data systems integrating learning, workforce, customer, and operational platforms.
• Develop Databricks-native applications, data pipelines, APIs, automations, and user experiences.
• Establish engineering practices for reliability, observability, testing, release management, access control, and lifecycle ownership.
• Define integration strategies across learning management systems, lab environments, content platforms, identity systems, and internal data products.
• Partner with business leaders on product direction, roadmaps, trade-offs, and measurable outcomes.
• Address security, privacy, compliance, governance, and responsible AI requirements.
• Lead cross-functional technical programs involving multiple engineering teams and senior stakeholders.
• Mentor engineers, contribute to hiring, and create reusable engineering patterns.
• Provide technical thought leadership through architecture documents, presentations, guidance, and community engagement.

Requirements

• 5–7 years of experience designing, building, and operating production software or data platforms, with experience leading work across organizational boundaries.
• Experience owning architecture and technical direction for complex, multi-system products.
• Strong full-stack or platform engineering background, including backend services, APIs, data pipelines, cloud infrastructure, and modern web applications.
• Experience designing and operating AI- or data-intensive systems such as recommendation engines, inference pipelines, search, personalization, or analytics products.
• Fluency in Python or a similar production programming language, strong SQL, and experience with relational and analytical data systems.
• Experience with cloud platforms, distributed systems, identity and access management, security controls, and production operations.
• Strong judgment regarding data privacy, governance, least-privilege access, and responsible AI.
• Ability to communicate with engineers, product managers, security and legal partners, and senior business leaders.
• Track record of delivering results with minimal oversight while bringing structure to ambiguous problems.
• Collaborative leadership style combining technical depth, product thinking, pragmatism, and bias for action.

Preferred Qualifications

• Experience with Databricks Apps, Lakehouse architectures, Unity Catalog, Delta Lake, Lakebase, Workflows, SQL Warehouses, or the Databricks SDK.
• Experience integrating learning, content, assessment, identity, HR, or customer-facing platforms.
• Experience building systems for internal users, external customers, or partners.
• Experience scaling automation and operational workflows across large user populations or high-volume data sources.
• Experience with React and Flask or FastAPI.
• Experience mentoring senior engineers or leading technical programs across multiple teams.
• Technical thought leadership through conference talks, publications, open-source work, or industry communities.

Compensation and Benefits

• ATS-listed salary range: $111,200–$191,050 USD.
• Compensation may include an annual performance bonus, equity, and benefits.
• Comprehensive benefits and perks are offered; details vary by region.