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About the Role

Lead the development and production deployment of advanced ML and AI solutions that deliver measurable business impact. This role requires a proven track record of taking models from problem definition through production deployment, adoption, monitoring, and continuous improvement.

Responsibilities

• Identify high-value business problems where machine learning, generative AI, or multi-agent systems can improve revenue, cost, risk, productivity, customer experience, or operational performance.
• Translate ambiguous business objectives into well-defined analytical problems, measurable success criteria, model evaluation plans, deployment strategies, and adoption metrics.
• Design, develop, validate, and deploy production-grade machine learning models across various use cases.
• Build and deploy generative AI and multi-agent solutions that coordinate specialized agents, tools, APIs, retrieval systems, workflows, and business rules.
• Design agentic systems with clear task decomposition, tool permissions, state management, memory boundaries, error handling, evaluation, observability, and human escalation paths.
• Develop solutions that operate reliably across structured, semi-structured, and unstructured data.
• Engineer robust data and feature pipelines using batch, streaming, CDC, and event-driven patterns.
• Build lakehouse and data mesh solutions using Delta Lake, medallion architecture, domain-oriented data products, Unity Catalog, governed workspaces, and environment separation.
• Architect scalable cloud-based AI solutions using Microsoft Azure, Databricks, Amazon Web Services, and Google Cloud Platform.
• Design for cloud portability and resilience.
• Apply strong software engineering practices, including modular design, unit and integration testing, code review, version control, CI/CD, containerization, infrastructure automation, API design, secure secrets management, and production release discipline.
• Implement MLOps and LLMOps practices for dataset, feature, model, prompt, agent, and evaluation versioning; automated testing; deployment; monitoring; drift detection; performance evaluation; cost management; and rollback.
• Establish AI evaluation frameworks that measure factuality, relevance, groundedness, safety, bias, robustness, latency, cost, tool-call accuracy, task completion, and business usefulness.
• Implement appropriate safeguards for AI systems, including security, privacy, access control, responsible AI, explainability, auditability, data classification, model governance, and compliance requirements.
• Evaluate models and AI systems using both technical metrics and business outcomes.
• Conduct controlled experiments, pilot deployments, A/B tests, champion-challenger evaluations, and post-launch assessments to validate whether solutions produce sustained business value.
• Diagnose model, data, pipeline, architecture, and production issues and lead remediation through root-cause analysis and cross-functional collaboration.
• Present technical findings, model behavior, limitations, risks, architecture decisions, and recommendations to business and executive stakeholders.
• Explain business priorities and operational requirements to technical teams and translate them into effective data, modeling, architecture, and delivery decisions.
• Mentor other data scientists and engineers by promoting sound modeling practices, production discipline, technical quality, documentation, and continuous learning.
• Contribute to the strategic roadmap for machine learning, generative AI, and multi-agent capabilities.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related technical field; advanced degree preferred.
• 5+ years of experience developing and deploying machine learning or artificial intelligence solutions in production environments.
• Demonstrated success delivering ML or AI solutions that generated measurable business impact.
• Strong experience with the complete machine learning lifecycle.
• Experience developing production systems with Python, SQL, PySpark, and common machine learning frameworks and libraries.
• Strong understanding of statistical modeling, machine learning algorithms, experimental design, model evaluation, uncertainty, explainability, and performance trade-offs.
• Proven ability to build solutions using complex and imperfect data.
• Experience designing and deploying cloud-based solutions using one or more of Microsoft Azure, Databricks, Amazon Web Services, or Google Cloud Platform; strong experience across multiple platforms is preferred.
• Experience with distributed data processing, data pipelines, feature stores, model registries, model serving, APIs, orchestration, and scalable compute environments.
• Experience with modern generative AI architectures.
• Experience designing or deploying multi-agent AI solutions.
• Strong knowledge of production engineering practices.
• Ability to design secure AI systems using identity and access management, least privilege, secrets management, encryption, private endpoints, network controls, data classification, and audit logging.
• Experience communicating technical concepts, model outputs, risks, architecture decisions, and recommendations to nontechnical stakeholders.
• Demonstrated ability to work independently, manage ambiguity, influence decisions, and deliver results in a cross-functional environment.
• Master’s or Ph.D. in a relevant technical discipline.
• Experience with Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Azure Databricks, Databricks Mosaic AI, MLflow, Unity Catalog, Databricks Model Serving, Vector Search, Lakeflow, or Databricks AI Gateway.
• Experience with GCP Vertex AI, Gemini, Vertex AI Model Garden, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, GKE, Cloud SQL, Cloud IAM, and Google Cloud Monitoring.
• Experience with AWS SageMaker, Amazon Bedrock, S3, Glue, EMR, EKS, Lambda, Step Functions, CloudWatch, or comparable AWS services.
• Experience with lakehouse and data mesh architectures.
• Experience with enterprise data governance and quality tooling.
• Experience with enterprise AI gateways, model routing, provider abstraction, LLM observability, prompt management, agent evaluation, and multi-model deployment patterns.
• Experience with time-series forecasting, optimization, causal inference, simulation, reinforcement learning, recommender systems, NLP, computer vision, or large-scale deep learning.
• Experience working with Google Workspace, including Google Drive, Docs, Sheets, Slides, Meet, Gmail, and shared collaboration workflows; experience automating or integrating Google Workspace APIs is a plus.
• Experience working with Google Cloud migration, modernization, or interoperability initiatives.
• Publications, patents, open-source contributions, technical presentations, or other evidence of advanced expertise in machine learning or artificial intelligence.

Skills

• Python
• SQL
• PySpark
• pandas
• NumPy
• SciPy
• scikit-learn
• XGBoost
• LightGBM
• TensorFlow
• PyTorch
• Azure Databricks
• Databricks Lakehouse
• Apache Spark
• Delta Lake
• Delta Sharing
• Unity Catalog
• Databricks SQL
• Lakeflow Declarative Pipelines
• Databricks Workflows
• Lakebase
• MLflow
• Mosaic AI
• Model Serving
• Vector Search
• AI Gateway
• Databricks Genie
• Azure Data Lake Storage Gen2
• Azure Machine Learning
• Azure OpenAI
• Azure AI Foundry
• Azure Event Hubs
• Azure Data Factory
• Azure Functions
• Azure Kubernetes Service
• Azure Container Apps
• Azure Key Vault
• Azure Monitor
• Application Insights
• Microsoft Defender for Cloud
• Azure API Management
• Entra ID
• Vertex AI
• Gemini
• Vertex AI Model Garden
• BigQuery
• Cloud Storage
• Dataflow
• Pub/Sub
• Cloud Run
• Google Kubernetes Engine
• Cloud SQL
• Secret Manager
• Cloud IAM
• Cloud Logging
• Cloud Monitoring
• Amazon SageMaker
• Amazon Bedrock
• S3
• Glue
• Athena
• Redshift
• EMR
• Lambda
• EKS
• Step Functions
• CloudWatch
• IAM
• large language models
• foundation models
• embeddings
• vector databases
• retrieval-augmented generation
• prompt engineering
• structured outputs
• function calling
• tool use
• agent orchestration
• workflow engines
• evaluation frameworks
• guardrails
• model routing
• human-in-the-loop controls
• Fivetran
• change data capture
• Auto Loader
• APIs
• batch and streaming ingestion
• data contracts
• schema enforcement
• data quality checks
• data lineage
• data catalogs
• access controls
• row- and column-level security
• governed data products
• GitHub
• GitHub Actions
• Azure DevOps
• CI/CD
• Terraform
• Docker
• Kubernetes
• Helm
• REST APIs
• FastAPI
• OpenAPI
• microservices
• infrastructure as code
• automated testing
• feature flags
• release management
• OpenTelemetry
• Datadog
• centralized logging
• model performance monitoring
• data drift detection
• concept drift detection
• latency monitoring
• cost monitoring
• incident response
• Power BI
• semantic models
• dashboards
• embedded AI experiences

Work Type

• Hybrid

Experience Level

• Senior
• 5+ years

Education Level

• Bachelor's degree
• Master's degree
• Ph.D.

Salary/Compensations

• $159,800–$244,300

Benefits

• Medical
• Dental
• Vision
• Health Savings Account
• Flexible Spending Accounts
• Retirement savings plan
• Life insurance
• Paid vacation and holidays
• Tuition assistance
• Employee assistance
• GM vehicle discounts

About the Company

• Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
• We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Equal Opportunity

• General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging.
• We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
• All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
• We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities.
• Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment.
• General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580.