Note: The job is a remote job and is open to candidates in USA. CareScout is on a mission to simplify and dignify the aging experience. They are seeking a Lead AI Engineer to design, build, and scale intelligent systems that power their product, operations, and analytics, collaborating closely with various teams to develop production-grade machine learning models and AI-driven solutions.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- Work closely with product, engineering, and business teams to identify ML opportunities, refine requirements, and deliver measurable outcomes
- Participate in architecture reviews, technical planning sessions, and roadmap discussions
- Document work in a way that is scalable and easy for future engineers to adopt
- Stay up to date on emerging ML frameworks, LLM advancements, Databricks capabilities, and scalable architecture patterns
- Explore new tools, libraries, and platforms that can enhance model performance or development efficiency
Skills
- 7+ years of experience in machine learning, applied AI, or similar engineering roles
- Strong expertise building ML models with Python, Spark, Databricks, and MLflow
- Deep knowledge of modern ML techniques: supervised/unsupervised models, deep learning, transformers, embeddings, vector stores, and LLM-based systems
- Solid understanding of software engineering principles: version control, testing, CI/CD, observability, and modular architecture
- Experience deploying ML models to production with reliable pipelines and monitoring
- Strong ability to explain technical concepts to non-technical stakeholders
- Experience working in agile product environments
- Proficiency with SQL and working with large-scale distributed datasets
- Experience with Databricks Model Serving, Unity Catalog, Feature Store, and Delta Live Tables
- Experience building LLM-powered applications, RAG systems, fine-tuning, or model distillation
- Familiarity with cloud infrastructure (AWS and Azure), Kubernetes, and container orchestration
- Background in statistics, computer science, machine learning engineering, or related fields
- Strong interest in building foundational ML platforms, tools, and frameworks for internal teams
- Experience with real-time ML systems, streaming data, or event-driven architectures
Benefits
- Competitive Compensation & Total Rewards Incentives
- Comprehensive Healthcare Coverage
- Multiple 401(k) Savings Plan Options
- Auto Enrollment in Employer-Directed Retirement Account Feature (100% employer-funded!)
- Generous Paid Time Off – Including 12 Paid Holidays, Volunteer Time Off and Paid Family Leave
- Disability, Life, and Long Term Care Insurance
- Tuition Reimbursement, Student Loan Repayment and Training & Certification Support
- Wellness support including gym membership reimbursement and Employee Assistance Program resources (work/life support, financial & legal management)
- Caregiver and Mental Health Support Services
Company Overview