Lone Wolf TechnologiesRemote
Staff Engineer, Innovation
United States of America
Full Time
2 weeks ago
H1B Sponsor Likely
Key skills
Software engineeringML/AI model productionizationData pipeline developmentSnowflakeS3KafkaCloud-native architectureAWS LambdaAWS BatchAWS S3AWS SageMakerAWS BedrockServerless architectureEvent-driven systemsBackend servicesJavaSpring BootAPI developmentPlatform/infrastructure engineeringLLM integrationPrompt engineeringRAG architecturesAgent frameworksJudgment on tradeoffsAIMLLLMRAGSpringAWSServerlessLambdaSageMakerBedrockMentoring
About this role
Lone Wolf Technologies is focused on integrating AI capabilities into their platform to enhance the way real estate professionals manage transactions. As a Staff Engineer, you will be responsible for designing the infrastructure and data pipelines that support AI/ML models, ensuring they are reliable and scalable for product features used by many professionals in the industry.
Responsibilities:
- 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
Requirements:
- 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
- Experience with real estate technology, MLS data, or proptech platforms
- Familiarity with LLM integration patterns — prompt engineering, RAG architectures, agent frameworks
- Background in data-intensive optimization domains
- Track record of mentoring engineers and raising the technical bar on teams you work with
- Experience at high-growth or early-stage companies where you wore multiple hats