Senior Machine Learning and Artificial Intelligence Scientist

Overview In this role you will lead the end-to-end development and production deployment of ML/AI solutions that drive measurable business impact. You will design generative AI and multi-agent systems on cloud-native architectures and partner with business leaders and engineers to translate needs into scalable AI products. You’ll operate across Azure, Databricks, AWS, and GCP, delivering secure, maintainable AI capabilities and guiding continuous improvement. This is a hands-on, cross-functional role shaping AI at scale within GM’s hybrid environment. Compensation / Benefitsmedical, dental, vision benefits Health Savings Account and Flexible Spending Accountsretirement savings planlife insurancepaid vacation and holidaystuition assistance Responsibilities Identify high-value problems where ML/AI can improve revenue, cost, risk, or customer experience Translate business objectives into clearly defined analytical plans, success criteria, and deployment strategies Design, develop, validate, and deploy production-grade ML models across diverse use cases Build and deploy generative AI and multi-agent solutions coordinating agents, tools, and workflows Design agentic systems with clear task decomposition, memory, error handling, and human escalation paths Engineer robust data/feature pipelines with reproducibility, lineage, and versioning Build lakehouse/data mesh solutions with Delta Lake, Unity Catalog, and governed workspaces Architect scalable cloud-based AI solutions across Azure, Databricks, AWS, and GCPApply strong software engineering practices (CI/CD, testing, containerization, IaC)Implement MLOps/LLMOps for versioning, testing, deployment, monitoring, drift detection, cost management, rollback Establish AI evaluation frameworks measuring factuality, safety, bias, latency, and business usefulness Implement safeguards for security, privacy, governance, explainability, and compliance Evaluate models with technical and business outcomes; conduct controlled experiments and post-launch assessments Present findings and architecture decisions to non-technical stakeholders in decision-oriented language Mentor peers in modeling best practices and production discipline Contribute to strategic roadmaps for ML/AI and reusable components Jump-start governance, data quality, and enterprise data sharing practices Support cross-functional collaboration and cross-cloud compatibility Key requirements 5+ years in developing and deploying ML/AI solutions in production Experience across full ML lifecycle: problem formulation to decommissioning Proficiency with Python, SQL, PySpark and ML libraries Strong grounding in statistical modeling, experimental design, and model evaluation Experience with cloud-based solutions across Azure, Databricks, AWS, or GCPKnowledge of data pipelines, feature stores, model registries, model serving, APIs, and orchestration Experience with modern generative AI architectures and multi-agent systems Strong production engineering practices: Git, CI/CD, containers, IaC, observability Ability to communicate complex concepts to non-technical stakeholders Ability to work independently, handle ambiguity, influence decisions Experience with secure AI systems, IAM, secrets management, encryption and audit logging Demonstrated track record of delivering business value through ML/AIstrong communicationcross-functional collaborationmentoring PythonSQLPySpark