Lead AI Engineer

Be Part Of A High-Performing Team: Join a mission-driven technology organization focused on improving the aging and long-term care experience for families, caregivers, and care seekers. This team is building modern digital solutions that bring together care options, resources, education, and human support into a more connected and accessible experience. The environment is collaborative, product-focused, and purpose-driven, with a strong emphasis on learning, inclusion, and building technology that creates meaningful real-world impact. What\'s In Store For You: This is a full-time opportunity available to candidates in Richmond, VA on a hybrid basis or remote candidates residing in approved Eastern Time Zone states. The role offers the opportunity to work on production-grade AI and machine learning solutions, contribute to scalable ML foundations, and partner closely with product, engineering, platform, data, and business stakeholders. Employees are offered comprehensive benefits, retirement savings options, generous paid time off, paid holidays, paid family leave, wellness support, tuition reimbursement, student loan repayment, and training/certification support. This role is not eligible for employment visa sponsorship. How You Will Make An Impact Design, build, train, evaluate, and deploy machine learning models for predictive analytics, classification, NLP, anomaly detection, generative AI, and other applied AI use cases. Develop production-ready AI/ML solutions on a Databricks Lakehouse platform using Python, Spark, MLflow, Delta Lake, and related tools. Build scalable feature pipelines, training workflows, validation processes, and model refresh cycles that are automated, reproducible, and reliable. Own the end-to-end ML lifecycle, including experimentation, model registry, deployment, monitoring, drift detection, model performance tracking, and ongoing optimization. Design modular ML architectures that integrate with APIs, data warehouses, microservices, and downstream applications. Develop LLM-powered applications, prompt engineering strategies, retrieval-augmented generation systems, embeddings, vector search, and related AI capabilities where appropriate. Partner with data engineering, product, platform engineering, and business stakeholders to turn ambiguous business opportunities into measurable AI/ML outcomes. Support experimentation through A/B testing, offline and online evaluation frameworks, statistical validation, and clear communication of results. Document models, systems, decisions, and workflows in a way that enables future engineers and cross-functional teams to adopt and maintain solutions. Are you an experienced ML/AI engineering professional ready to build production-grade intelligent systems? 7+ years of experience in machine learning, applied AI, machine learning engineering, or a similar hands-on technical role. Strong hands-on expertise with Python, Spark, Databricks, MLflow, SQL, and large-scale distributed datasets. Experience building, deploying, and monitoring machine learning models in production environments. Strong understanding of modern ML techniques, including supervised learning, unsupervised learning, deep learning, transformers, embeddings, vector stores, and LLM-based systems. Experience designing reproducible ML pipelines, CI/CD workflows, model deployment patterns, and observability practices. Solid software engineering foundation, including version control, testing, modular architecture, maintainability, and production reliability. Ability to communicate technical concepts clearly to non-technical stakeholders and influence technical direction across teams. Experience working in agile product environments with product managers, engineers, data teams, and business partners. Nice to have: Databricks Model Serving, Unity Catalog, Feature Store, Delta Live Tables, RAG systems, LLM fine-tuning, model distillation, AWS, Azure, Kubernetes, containers, real-time ML, streaming data, or event-driven architectures. Strong curiosity, collaboration skills, ownership mindset, and ability to work through ambiguity with incomplete data or evolving requirements. #J-18808-Ljbffr