Senior ML/AI Engineer

About CareScout Join us on a mission to simplify and dignify the aging experience. We are the children, siblings, neighbors, and friends of those navigating the fragmented and confusing system of long-term care. Our team is ferociously curious and relentless in our pursuit of a better system – and we are deeply committed to a sense of belonging for all, in all phases of life. We’re creating a new experience for care seekers and their families, bringing together long-term care options, non-healthcare resources,

, and human support into one place. We work hard, we have fun, we care about each other, and we share the mission. If this sounds like a place where you could thrive, join us! CareScout is a wholly owned subsidiary of Genworth Financial, Inc, a Fortune 500 provider of products, services and solutions that help families address the financial challenges of aging. Our four values guide our strategy, our decisions, and our interactions: • Make it human. We care about the people that make up our customers, colleagues, and communities. • Make it about others. We do what’s best for our customers and collaborate to drive progress. • Make it happen. We work with intention toward a common purpose and forge ways forward together. • Make it better. We create fulfilling purpose-driven careers by learning from the world and each other. POSITION TITLE Senior ML/AI Engineer POSITION LOCATION This position is available to candidates in Richmond, VA (Hybrid) or remote applicants residing in states/locations under Eastern Standard Time: Connecticut, Delaware, Florida, Georgia, Indiana, Kentucky, Maine, Maryland, Massachusetts, Michigan, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, Washington DC, or West Virginia

We are seeking a highly skilled and experienced Senior AI/ML Engineer to join our growing data and machine learning organization. In this role, you will design, build, and scale intelligent systems that power our product, operations, and analytics. You will work closely with data engineers, product managers, platform engineers, and business stakeholders to develop production‑grade machine learning models and AI-driven solutions on top of our Databricks Lakehouse platform. A successful candidate is both an innovative ML practitioner and a strong hands-on engineer who can take projects from concept to production. You are comfortable navigating ambiguity, working with incomplete data, leading technical discussions, and implementing systems that are robust, observable, and maintainable. You thrive in collaborative environments and enjoy building scalable ML foundations that accelerate development across teams. What You’ll Do Model Development & Applied Machine Learning • Build, train, evaluate, and deploy machine learning models for prediction, classification, NLP, anomaly detection, and generative AI use cases. • Apply modern ML techniques, experimentation frameworks, and statistical best practices to ensure model accuracy, fairness, and reliability. • Develop LLM-driven applications, prompt engineering strategies, and retrieval-augmented generation (RAG) systems when applicable. Data Engineering & Feature Development • Design and implement scalable features using Delta Lake, Spark, and Databricks Feature Store. • Partner with data engineering teams to understand data availability, quality, lineage, and ingestion patterns. • Build automated, reproducible pipelines that support training, validation, and model refresh cycles. MLOps & Productionization • Own end-to-end ML lifecycle using Databricks workflows, MLflow, feature stores, and model registries. • Develop CI/CD and automated model deployment pipelines that ensure performance and reliability. • Implement monitoring for drift, model degradation, data quality, and performance regressions. AI Systems Architecture • Design modular, scalable ML architectures that integrate with APIs, data warehouses, microservices, and downstream applications. • Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business constraints. Experimentation & Evaluation • Develop A/B tests, offline/online evaluation frameworks, and statistical validation strategies. • Analyze model results with clarity and communicate insights to technical and non-technical partners. Cross-Functional Collaboration • Work closely with product, engineering, and business teams to identify ML opportunities, refine