Top AI & Machine Learning Jobs

Deploys Medeloop’s AI systems into customer cloud environments, builds data and EHR connectors, and creates clinical and research workflows. Owns deployments from scoping through production, including evaluation, testing, monitoring, security reviews, and customer collaboration. Works with sensitive patient data under HIPAA, explains infrastructure and access controls to security teams, and converts custom implementations into reusable platform features.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.

Own the end-to-end lifecycle of generative AI products across Success Academy’s school network. Translate stakeholder and business needs into AI/ML requirements, collaborate with engineering, data science, and UX teams, integrate LLMs and RAG pipelines, define evaluation metrics, run experiments, build user feedback loops, and optimize products for measurable efficiency and student outcomes. Contribute to enterprise AI strategy and manage products from discovery through launch.

Support a government customer as an expert AI/ML Engineer by developing advanced AI/ML architectures, responsible AI implementations, enterprise deployment strategies, and roadmaps advancing technologies from research to operational deployment. The role requires expertise in neural networks, reinforcement learning, model interpretability, MLOps pipelines, productionizing data science research, and deploying GPU-enabled deep learning applications.

Leads the AI Governance Program for a regulated mortgage lending division. Establishes governance structures, policies, risk-tiering methods, controls, use-case lifecycle processes, third-party AI oversight, and audit-ready documentation. Coordinates reviews across legal, compliance, security, privacy, data governance, model risk, vendor management, and enterprise risk teams. Reports AI governance KPIs and KRIs to senior leadership while providing independent challenge and ensuring AI systems meet regulatory and organizational requirements.

Design and build production AI applications, agentic workflows, RAG pipelines, and multi-step automations using LLM APIs and orchestration frameworks. Integrate tools, retrieval systems, prompts, APIs, and infrastructure; create evaluation and observability systems; monitor reliability, latency, cost, and output quality. Translate ambiguous business needs into practical technical solutions, collaborate with stakeholders, and identify opportunities for AI-enabled process improvement.

Own Firecrawl’s internal AI stack, including agent harnesses, shared skills, configurations, model routing, MCP servers, hooks, evals, and tooling. Drive AI-agent adoption across teams, beginning with revenue, by building workflows, coaching employees, and overcoming resistance. Analyze token spend and output to improve cost per useful token through model selection, routing, caching, and workflow optimization. Build missing capabilities in code and stay current with rapidly changing AI development tools.

Leads Virginia Union University's enterprise AI strategy, governance, training, adoption, and responsible implementation. Develops standards, policies, risk controls, and institutional AI literacy programs while guiding the design, deployment, and monitoring of AI agents, automations, and retrieval-augmented systems. Partners with academic and administrative divisions to identify use cases, redesign processes, integrate approved platforms, measure impact, manage vendors, pursue partnerships and grants, and build an expanding AI team.

Leads Jabil’s enterprise AI strategy, architecture, governance, and adoption roadmap. Defines reference architectures and oversees initiatives involving generative AI, computer vision, digital twins, machine learning, intelligent automation, and MLOps. Advises executives, evaluates technologies, leads distributed technical teams, manages proofs of concept and deployments, establishes responsible AI practices, and drives innovation through patents, publications, training, and strategic investments.