Role Description 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 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. - Continuous Learning - 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. Qualifications - 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. Nice to Have - 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