Top AI & Machine Learning Jobs

Develops and operationalizes AI and data governance policies, standards, procedures, checklists, decision trees, and reporting templates. Translates frameworks including NIST AI RMF, the EU AI Act, and Colorado ADMTA into practical workflows. Partners with engineering, data science, product, governance, risk, and business teams to test standards, define metadata and logging requirements, and enable cross-functional execution through version-controlled policy frameworks.

Support the design, development, and productionization of autonomous AI services and multi-agent workflows. Assist with integrating agent communication protocols (MCP, A2A), hybrid control flows, sandboxed tool execution, and scalable microservice architectures. Collaborate with cross-functional teams under senior guidance to translate business requirements into technical agent specifications and deploy resilient LLM-powered systems.

Design and deliver scalable Databricks/Spark/Delta Lake data pipelines, implement GitLab CI/CD and IaC/container workflows, apply SOLID engineering and testing frameworks, mentor peers, integrate AI-driven monitoring, and collaborate with Data Science and Product to productionize ML solutions on AWS while ensuring security and reliability.

Design and deploy production-grade ML models and GenAI/LLM solutions to improve customer segmentation, churn prediction, LTV estimation, and automate enterprise workflows. Build cloud-native data science pipelines, measure impact via experiments, create executive dashboards, and mentor peers on MLOps and data storytelling.

Engineer serverless AWS utilities and integrate Generative/Agentic AI into QA automation. Build APIs, prototypes, and CI/CD-backed tooling to accelerate test coverage, improve fault detection, and collaborate with global engineering teams to deploy reliable automation solutions.

Scale and secure the enterprise Databricks platform on AWS for production analytics, ML, and AI agent workloads. Administer workspaces, Unity Catalog governance, CI/CD pipelines, Terraform automation, MCP server integration for agent frameworks, cluster policy enforcement, cost attribution, monitoring, incident response, and mentor junior engineers.

Embed into engineering intake to standardize cloud cost estimation, translate technical requirements into predictive cost models, track ROI for AI initiatives, and recommend cost-avoidance strategies while building reporting and value-capture frameworks across project lifecycles.

Drive deployment of AI solutions for field services: prepare data governance, integrate knowledge-base chatbots, enable wearable AI and AR onboarding, build agentic "AI manager" workflows, and optimize prompts and automation to improve technician productivity and service quality.

Lead automation and measurement of the AI Governance program by building self-service dashboards, tracking Time-to-Approval and throughput metrics, analyzing trends to remove bottlenecks, producing C-suite evidence packs, and recommending process improvements to increase governance maturity and delivery velocity.

Serve as the bridge between AI models and field operations to implement AI-driven tools (chatbots, wearables, AR, agentic workflows). Prepare AI-ready data, enforce governance, deploy and iterate knowledge-base chatbots, design AR onboarding, create an "AI manager" for technicians, and build prompt libraries and automation to optimize workflows and improve field technician efficiency and service quality.