Lumenalta is seeking experienced AI Engineers to design and deploy production-grade AI systems for community banks, mortgage, and insurance institutions, to build technology solutions that scale, delight users, and accelerate business growth
Requirements
- Strong backend Python
- Expertise with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
- Familiarity with modern data pipeline tools (Airflow, Spark, Kafka) and workflow orchestration frameworks such as n8n or LangGraph
- Background in enterprise applications such as chatbots, workflow automation, RAG pipelines, or intelligent document processing
- Understanding of regulated industries (financial services, insurance, or similar) with the ability to design compliance-aligned models
- Experience with AI/ML engineering with a track record of deploying AI into production at scale
- Familiarity with collaboration tools and version control systems
Responsibilities
- Design, build, and deploy AI models into production (not just prototypes or wrappers)
- Develop and maintain backend Python services that power intelligent automation workflows
- Implement enterprise-scale data pipelines for ingesting and processing financial and regulatory documents, with automated data quality checks and monitoring
- Train and optimize models on banking rules, compliance standards, and regulatory datasets
- Build intelligent routing systems (e.g., auto-approval for simple loans/accounts, escalation to humans for complex cases)
- Collaborate with engineering leadership to shape architecture, delivery models, and stack decisions
- Ensure compliance-first design, embedding regulatory considerations into every product feature
Other
- 3–5+ years in AI/ML engineering with a track record of deploying AI into production at scale
- 100% Remote; please ensure you have a comfortable office set at your desired work location
- Based in regions that align with the Pacific, Central or Eastern U.S. time zones
- Minimum of 40 hours per week, Monday through Friday
- Bachelor's degree or higher in a relevant field (not explicitly mentioned but implied)
