Top Artificial Intelligence Jobs

The AI and Automation Solutions Developer architects, builds, tests, deploys, and maintains production-ready AI applications, agents, integrations, and automated workflows. The role develops Power Platform solutions, APIs, enterprise integrations, and internal applications; implements security, privacy, governance, monitoring, and documentation; evaluates emerging AI technologies; measures operational impact; and partners with data, clinical, IT, quality, operations, and vendor teams to deliver scalable healthcare solutions.

Supports AI Engineers in designing, developing, testing, validating, and documenting AI-driven solutions using Microsoft's Azure AI ecosystem. The intern helps build agentic and automation solutions, contributes to production testing and internal knowledge bases, participates in team collaboration, and partners with business stakeholders to identify automation opportunities. The role offers hands-on experience with enterprise AI, machine learning, custom copilots, and unstructured data.

Leads enterprise AI architecture strategy, standards, governance, and roadmaps across products, platforms, data, and business processes. Guides solutions involving generative AI, agentic AI, RAG, machine learning, automation, cloud platforms, and AI infrastructure. Partners with executives and technical teams on architecture reviews, security, compliance, scalability, and cost optimization; mentors senior engineers and provides hands-on leadership through prototypes and critical problem resolution.

Manage the full lifecycle of commercial contracts, including SaaS subscriptions, licensing agreements, procurement contracts, and partnerships. Draft, review, redline, and negotiate agreements; assess legal and commercial risk; support privacy and security terms; and collaborate with Sales, Finance, Product, Security, and Revenue Operations. Maintain CLM records, templates, workflows, metrics, renewals, and obligations while supporting process improvement, user training, and contracting compliance.

Leads analytics across dermatology claims, EMR, revenue cycle, and operational datasets. Identifies revenue leakage, denial trends, coding opportunities, and reimbursement improvements; develops AI-assisted workflows, predictive models, anomaly detection, dashboards, forecasts, and KPI reporting. Partners with finance, managed care, clinical operations, IT, and executive leadership on payer performance, budgeting, strategic planning, and healthcare analytics governance.

Designs, builds, deploys, and operationalizes enterprise generative AI solutions, including LLM applications, RAG architectures, autonomous agents, MCP servers, and AI integrations. Partners with stakeholders, engineering, infrastructure, security, and data teams to deliver secure, governed, scalable AI systems. Develops AI tools and workflows, optimizes data processes, provides architectural guidance, and supports high-impact business use cases.

Build and deploy AI solutions across engineering and construction project lifecycles, including estimating, design, scheduling, procurement, field execution, and closeout. Train project teams, lead live-project pilots, configure agentic AI tools, integrate solutions with technology systems, apply governance standards, and quantify business impact. The role requires hands-on LLM development, technical implementation, project lifecycle knowledge, and the ability to support nontechnical teams in office and field environments.

Builds and deploys generative AI solutions for finance, accounting, HR, IT, legal, and procurement. The role configures Microsoft and Anthropic AI tools, develops workflow automations, trains nontechnical users, drives adoption through pilots and support, applies governance standards, and measures business impact through time, cycle-time, error, and rework reductions. It also collaborates with peer AI leads to share reusable solutions across the company.

Build and deploy AI solutions for installation and maintenance operations, including maintenance workflows, dispatch, asset performance, quality, safety, and technician enablement. Train field and operations teams, deploy agentic AI tools, run adoption pilots, integrate operational systems, apply governance standards, and measure business impact through metrics such as downtime, repair time, first-time-fix rates, energy performance, and service margin.

Leads the design, development, deployment, and optimization of AI-powered marketing workflows. Owns the Marketing AI and Automation backlog, designs LLM, RAG, and multi-agent systems, builds data foundations, and operationalizes secure, observable production solutions. Partners with marketing, Martech, analytics, and leadership teams to define use cases, metrics, integrations, guardrails, and enablement strategies.

Designs and leads enterprise AI and machine learning architecture for power trading, asset optimization, risk analysis, and decision support. Responsibilities include defining AI strategy, architecting scalable data and ML platforms, integrating cloud and data engineering technologies, overseeing MLOps and model governance, and guiding cross-functional engineering and analytics teams. The role also develops RAG, LLM orchestration, agent-based workflows, and real-time AI solutions while ensuring security, compliance, reliability, and effective executive communication.

Build trusted data models, define metrics, ensure data quality, conduct analyses, and create dashboards supporting Product, Finance, GTM, executives, and customers. Present actionable insights, connect GA4, Google Search Console, and AI Search data to business outcomes, and develop reusable reporting frameworks. The role requires strong SQL, AI-assisted analytics, customer communication, and comfort operating in a fast-growing startup.

Provides senior nursing expertise within a healthcare technology company, supporting commercial teams through customer demonstrations, presentations, and executive engagements. Acts as a clinical advocate for nursing solutions, translates clinical workflows into technology requirements, provides product feedback, supports adoption, and represents the company through thought leadership. Requires significant registered nursing experience, healthcare technology integration expertise, executive communication skills, and the ability to influence cross-functional teams. The role requires 30–50% travel.

Lead research and development of AI solutions for complex energy-sector challenges, including machine learning, deep learning, generative AI, and foundation models. Lead projects, build prototypes, mentor researchers, publish findings, file patents, write grant proposals, and collaborate with industry, academic, and government partners across energy, grids, robotics, manufacturing, and business applications.

Build and operate AI automation infrastructure, including n8n workflows, MCP servers, LLM integrations, agentic workflows, chat and voice agents, and AI-assisted engineering pipelines. Integrate CRM, ERP, support, fulfillment, and cloud data systems while maintaining security, measuring cost and performance, evaluating emerging tools, and presenting solutions to leadership and cross-functional teams.

Lead and execute AI/ML research for national security, focusing on model vulnerabilities, adversarial techniques, and model architecture. Translate mission needs into requirements, guide teams, publish research, and apply reverse-engineering and forensic methods as needed.

Develop and maintain backend features for Guidewire’s application platform and AI services. Build secure, scalable web services using AI coding tools, collaborate on platform operations, write tested code, participate in code reviews, and contribute to AI, cloud, and data platform adoption. Work with LLMs, RAG, vector databases, AI agents, MCP, Kubernetes, and infrastructure as code while supporting continuous improvement and developer experience.

Optimize cloud, SaaS, and AI financial operations by improving cost visibility, allocation, forecasting, and resource utilization. Monitor cloud and large language model spending, analyze GPU and usage-based cost drivers, build financial models, and identify savings opportunities. Implement tagging, showback, chargeback, and FinOps processes while partnering with technology, finance, and procurement teams. Educate stakeholders on cost-conscious consumption and support budgeting, commitment decisions, and vendor negotiations.

Leads full-stack software engineering within JPMorgan Chase’s supply chain technology team. Responsibilities include designing, developing, testing, debugging, and maintaining secure, scalable production systems; advancing AI-assisted engineering practices; automating recurring issue remediation; ensuring application resiliency and security; and evaluating vendor and internal technology solutions. Requires strong software development, cloud-native, agile, financial-services technology, and responsible AI expertise.

Leads independent evaluations, adversarial testing, research partnerships, and transparency programs for frontier AI safety. Coordinates researchers, engineers, product, policy, communications, and external experts; translates technical findings into safety recommendations and public documentation; manages goals, milestones, risks, dependencies, and follow-up actions.

Develop and improve reliable LLM and deep learning capabilities for healthcare applications. Lead research and implementation of scalable real-time machine learning models, Responsible AI metrics and practices, RAG and LLM strategies, and technical innovation agendas. Collaborate with AI scientists and engineers, establish best practices, and drive adoption across service teams.

Build, evaluate, optimize, and deploy machine learning models for healthcare applications. Analyze healthcare datasets, conduct A/B experiments and statistical analysis, establish scalable ML development and serving processes, research innovative AI approaches, and collaborate with software engineers. Mentor and recruit AI/ML professionals, present findings to leadership, and pursue intellectual property through cutting-edge AI research.

Lead and grow the AI Platform engineering team while remaining hands-on in production machine learning. Own matching, ranking, retrieval, personalization, agentic workflows, trust and safety, evaluation, and model quality, cost, and latency. Hire and coach engineers, set technical direction, translate business goals into roadmaps, and ship systems that improve recruiting outcomes. The role requires production ML or LLM experience, strong evaluation and data skills, technical leadership, and weekly coding involvement.

Lead intellectual property workstreams for acquisitions, investments, and complex commercial and research agreements. Conduct IP diligence, negotiate IP provisions, advise business and legal teams on IP risks, and develop scalable playbooks, templates, and processes. The role also contributes to broader IP strategy involving open source, trade secrets, patents, and emerging AI-related business models.

Build and deploy dependable AI agents, automations, controls, and external-facing solutions across finance, credit risk, servicing, collections, and operations. Partner with business users to scope workflows, design systems, write evaluations, instrument production processes, measure business impact, and drive adoption. Develop reusable agent patterns and harness standards while applying governance, human review, monitoring, and escalation controls.