Design and build the AI platform layer — the data pipelines, serving infrastructure, and integration patterns that connect ML models to Lone Wolf's products
Productionize AI/ML capabilities — take models from prototype to production, owning reliability, performance, and scalability
Architect data pipelines that ingest, transform, and serve data from Lone Wolf's ecosystem to power AI features
Set technical standards for AI engineering across the Innovation team — define patterns for model serving, feature stores, monitoring, and rollout strategies
Ensure models are designed for production constraints from the start, not retrofitted after the fact
Evaluate and integrate AI/ML tooling — LLM APIs, vector databases, orchestration frameworks, cloud AI services — making pragmatic build-vs-buy decisions
Influence technical direction across engineering teams, providing architectural guidance on how product teams should integrate AI capabilities
Prototype rapidly when needed — you're comfortable building end-to-end proof-of-concepts to validate feasibility before committing to full builds

8+ years of software engineering experience with increasing scope and technical complexity
Proven experience productionizing ML/AI models — you've taken data science output and made it work reliably in production at scale
Deep data pipeline expertise — you've built ingestion, transformation, and serving systems using tools like Snowflake, S3, Kafka, or similar
Strong cloud-native architecture skills — AWS preferred (Lambda, Batch, S3, SageMaker, Bedrock); comfortable designing serverless and event-driven systems
Full-stack technical range — backend services (Java/Spring Boot or similar), APIs, and enough frontend awareness to build internal tools or review UIs when needed
Experience working in platform/infrastructure roles where your work enables other teams to ship faster
Excellent judgment on tradeoffs — you know when to build robust and when to ship fast, and you can articulate why

Staff Engineer – Innovation

Key skills