Senior Machine Learning and Artificial Intelligence Scientist

Quick Facts

• Role: Senior Machine Learning and Artificial Intelligence Scientist

• Focus: Build and production-deploy advanced ML, generative AI, and multi-agent solutions with measurable business impact

Description

Lead the end-to-end development and production deployment of scalable AI products—from problem definition and experimentation to adoption, monitoring, and continuous improvement. Design and implement ML and generative AI/multi-agent solutions using complex, heterogeneous, imperfect data, partnering across business, product, data engineering, software engineering, cloud architecture, and technical stakeholders.

Responsibilities

• Identify high-value business problems where ML, generative AI, or multi-agent systems can improve revenue, cost, risk, productivity, customer experience, or operations

• Translate ambiguous objectives into analytical problems, success criteria, evaluation plans, deployment strategies, and adoption metrics

• Design, develop, validate, and deploy production ML models (forecasting, classification, regression, optimization, anomaly detection, recommendations, NLP, computer vision, time-series, etc.)

• Build and deploy generative AI and multi-agent solutions coordinating specialized agents, tools, APIs, retrieval systems, workflows, and business rules

• Design agentic systems with task decomposition, tool permissions, state management, memory boundaries, error handling, evaluation, observability, and human escalation

• Create solutions that work reliably across structured, semi-structured, and unstructured data with schema changes and data quality issues

• Engineer robust data and feature pipelines (batch, streaming, CDC, event-driven) ensuring reproducibility, lineage, validation, versioning, and reliable access

• Build lakehouse and data mesh solutions using Delta Lake, medallion architecture, domain-oriented data products, Unity Catalog, and governed workspaces across environments

• Architect scalable cloud-based AI solutions across Azure, Databricks, AWS, and GCP; design for portability and resilience when appropriate

• Apply strong software engineering practices: modular design, unit/integration testing, code review, version control, CI/CD, containerization, infrastructure automation, API design, secure secrets, and production release discipline

• Implement MLOps/LLMOps for versioning (datasets/features/models/prompts/agents/evaluations), automated testing, deployment, monitoring, drift detection, cost management, and rollback

• Establish AI evaluation frameworks (factuality, relevance, groundedness, safety, bias, robustness, latency, cost, tool-call accuracy, task completion, business usefulness)

• Implement safeguards for AI systems (security, privacy, access control, responsible AI, explainability, auditability, data classification, governance, compliance)

• Evaluate models with technical metrics and business outcomes; run experiments, pilots, A/B tests, champion-challenger, and post-launch assessments

• Diagnose production issues and lead remediation via root-cause analysis with cross-functional teams

• Communicate findings, risks, and architecture decisions in business-relevant terms; mentor engineers and contribute to the ML/GenAI/multi-agent roadmap

Benefits

• Health and wellbeing programs: medical, dental, vision, Health Savings Accounts, Flexible Spending Accounts, retirement savings, life insurance

• Paid vacation and holidays

• Tuition assistance

• Employee assistance

• GM vehicle discounts

Requirements

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related technical field; advanced degree preferred

• 5+ years of experience developing and deploying ML/AI in production environments

• Demonstrated production success delivering measurable business impact

• Experience across the complete ML lifecycle (problem formulation through decommissioning)

• Production experience with Python, SQL, PySpark, and common ML frameworks/libraries

• Strong statistical modeling and ML evaluation expertise (uncertainty, explainability, trade-offs)

• Ability to build with complex/imperfect data (disparate sources, evolving schemas, missing/noisy data, large-scale)

• Cloud experience deploying solutions using Azure/Databricks/AWS/GCP (multi-cloud preferred)

• Experience with distributed processing, data pipelines, feature/model registries, model serving, APIs, orchestration, and scalable compute

• Generative AI expertise: LLMs, RAG, embeddings, vector search, prompt engineering, tool use/function calling, structured outputs, agent orchestration

• Multi-agent system experience coordinating agents/tools/workflows

• Production engineering practices: Git, automated testing, CI/CD, containers, APIs, observability, infrastructure as code, reliability

• Ability to design secure AI systems (IAM/least privilege, secrets management, encryption, private endpoints, network controls, data classification, audit logging)

• Ability to communicate with nontechnical stakeholders and work independently in ambiguous, cross-functional settings