Top Artificial Intelligence Jobs

Researches, designs, develops, deploys, integrates, evaluates, and tests AI/ML and Generative AI solutions for mission-focused applications. Builds LLM-based systems using retrieval-augmented generation, agentic workflows, cloud services, and AI/ML frameworks. Participates in the full software engineering lifecycle, collaborates with distributed Agile teams, and communicates technical concepts to technical and non-technical stakeholders.

Build scalable AI capabilities for Fidelity Wealth as a senior full-stack engineer. Partner with business, product, architecture, data, and platform teams to create secure, reliable solutions. Responsibilities include prototyping, developing production-ready software, applying scalable design patterns, contributing to architecture discussions, practicing test-first development, and delivering high-quality solutions using modern programming languages and cloud platforms.

Build scalable AI capabilities for Fidelity Wealth as a full stack engineer. Partner with business, product, architecture, data, and platform teams to develop secure, reliable solutions. Responsibilities include prototyping, production software development, applying software design principles, testing, problem-solving, participating in design discussions, and delivering maintainable systems using modern programming languages, cloud platforms, and generative AI tools.

Develops and validates deep learning computer vision models for cardiovascular medical imaging, including segmentation, landmark detection, measurements, screening, and clinical decision support. The role advances imaging foundation models, builds scalable data pipelines and MLOps monitoring, evaluates bias and generalizability, and supports verification, validation, risk assessment, regulatory documentation, and production deployment. Collaboration spans clinical, regulatory, quality, engineering, product, and business teams in a regulated healthcare environment.

Leads scientific and technical direction for AI across structural heart applications. Designs, trains, validates, and deploys advanced machine learning models, including foundation, generative, multimodal, clinical language, imaging, and waveform models. Evaluates emerging technologies, establishes scientific standards, mentors technical staff, and partners with engineering, clinical, quality, and regulatory teams on safe, fair, robust, and compliant AI products. Builds data pipelines and MLOps capabilities while supporting model validation, risk management, monitoring, and regulatory documentation.

Build and maintain scalable full-stack integrations between Commure and healthcare systems, including EHRs and insurance providers. Design end-to-end workflows, use LLMs to reverse-engineer EHRs, improve architecture and reliability, troubleshoot production issues, implement monitoring, and ensure HIPAA, HL7, and FHIR compliance. Collaborate with product, healthcare, DevOps, QA, and security teams throughout development and deployment.

Owns drafting and negotiation of AI, data, privacy, security, IP, and commercial contract terms for healthcare technology deals. Advises Sales, Client Success, product, and engineering on data rights, model training, PHI, HIPAA, licensing, and regulatory requirements. Negotiates customer, vendor, partner, SaaS, reseller, government subcontract, and technology agreements; manages deals through signature; maintains templates and playbooks; and escalates nonstandard privacy, compliance, and risk positions.

Research and map the global AI industry, including its subsectors, key companies, technologies, supply chains, and major developments. Maintain current industry knowledge through conferences, publications, and expert discussions. Partner with product and engineering teams to improve platform coverage, and write reports for customers, prospects, and internal stakeholders. Serve as WireScreen’s internal and external AI industry subject-matter expert.

Leads technical architecture and delivery of Agentforce-powered solutions across Salesforce, Data 360, AWS, and related platforms. Designs AI agents, data architectures, integrations, grounding strategies, automation, testing, security controls, and deployment approaches. Provides technical leadership through design and code reviews, mentoring, standards development, and customer collaboration. The role also contributes to reusable delivery assets, evaluates emerging AI capabilities, maintains Salesforce certifications, and requires travel of up to 25%.

Designs and leads implementation of Agentforce-powered solutions across Salesforce, Data 360, and related cloud platforms. Responsibilities include architecting AI agents, data and integration architectures, grounding and evaluation strategies, automation, testing, deployment, and security. Provides technical leadership through design and code reviews, mentoring, standards development, and customer collaboration. Requires strong Salesforce development expertise, AI and generative AI experience, REST integrations, DevOps practices, and communication skills. Travel up to 25% is required.

Leads enterprise AI, data, application, and product strategy; oversees AI adoption, governance, architecture, software development, enterprise applications, and data strategy. Partners with executives to prioritize technology investments, deliver measurable business outcomes, establish standards, manage portfolios, and guide organizational change. Leads teams through coaching, hiring, budgeting, performance management, and workforce planning.

Build, test, deploy, and productionize AI agents and applications using Microsoft’s AI and cloud stack. Lead customer engagements from discovery through deployment and handoff, applying security, data-boundary, performance, grounding, retrieval, workflow, and governance standards. Collaborate with customers, partners, architects, and practice leads; contribute reusable components, marketplace offerings, and enablement content. Coach engineers and partner delivery teams while helping refine reference architectures and delivery standards.

Leads the Axis AI applications pillar, owning its roadmap, IP, engineering team, agentic application layer, and enterprise AI-native SDLC. Establishes development standards, tooling, security guardrails, accreditation processes, and engineering metrics across business units. Builds mission AI applications and agents for federal customers, partners across technical pillars, supports acquisitions, engages program and executive stakeholders, and develops senior engineering talent. The role requires delivering secure, accredited, production software for defense, intelligence, and federal civilian missions.

Leads the enterprise AI roadmap and delivers secure, business-ready AI assistants, Copilot agents, workflow automations, document solutions, and integrations. Partners across business, IT, security, and compliance to identify opportunities, translate requirements, deploy and support solutions, establish governance, train employees, and measure adoption, value, risk, and performance.

Design exceptionally difficult, expert-level technical problems that expose limitations in frontier AI agents. Translate real challenges from coding, machine learning, systems, security, or hardware into tasks solved from scratch in a Linux terminal. Evaluate whether multiple leading models fail consistently. Contributors need advanced education, extensive professional experience, and published work, with an ongoing commitment of at least seven weeks.

Own the roadmap and lifecycle of AI-powered products, from problem definition through launch and iteration. Partner with engineering and data science to assess feasibility, define requirements and metrics, run experiments, and balance model quality, cost, latency, and user experience. The role requires hands-on AI prototyping or workflow automation, strong communication across technical and business teams, and accountability for product outcomes.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Leads Wasabi’s AI infrastructure, data factory, and developer partnerships strategy. Hunts and originates new relationships, develops joint go-to-market and co-selling motions, structures revenue-generating deals, and manages executive partner relationships. The role collaborates with Business Development, Sales, Marketing, and Product, builds the developer ecosystem, tracks pipeline and revenue performance, and represents Wasabi at industry events. Candidates need deep AI infrastructure and storage expertise, an established industry network, and a proven record of closing strategic partnerships.

Lead design and delivery of a scalable AI platform for Aladdin: build Kubernetes-based model deployment, cloud-native infrastructure, CI/CD for ML, ensure security/compliance, troubleshoot platform reliability, translate business needs into robust technical designs, and collaborate across global teams.

Lead regional execution of AI enablement: manage workflows, coaching, triage, experimentation, stakeholder coordination, and team development to drive adoption of firm AI products and scalable best practices.

Leads the people function through rapid company growth, partnering with the CEO on organizational design, executive hiring, workforce planning, compensation, equity, leveling, leadership development, performance management, talent acquisition, people operations, compliance, employee relations, and the transition from a PEO to internal HR infrastructure.

Leads MaintainX’s AI strategy, organization, and execution across agentic AI, machine learning, asset intelligence, work intelligence, and GTM intelligence. Builds scalable AI platforms, RAG systems, data and inference pipelines, predictive models, and workflow-integrated intelligence. Establishes technical vision, hires and develops AI leaders, partners cross-functionally, and positions the company as an AI leader in industrial operations.

Leads enterprise AI governance across the full AI lifecycle, including classification, risk assessment, inventory management, approval workflows, committee facilitation, and audit readiness. Partners with technology, security, data governance, model risk, third-party risk, and compliance teams to ensure responsible AI adoption aligned with regulatory expectations. Develops assessment methodologies, tracks conditional approvals, maintains AI governance documentation, and supports regulatory examinations.

Provide hands-on support for complex and regulated customer issues while improving AI-powered support systems, workflows, automation, and self-service experiences. Review AI agent performance, identify conversation patterns, develop support logic, manage escalations, and create policies and playbooks. The role requires strong ownership, quantitative thinking, and collaboration across product, engineering, and operations in an early-stage fintech startup.

Build AI agents, tools, data connectors, and multi-step workflows using Python, APIs, SQL, and agent frameworks. Develop retrieval strategies, evaluation sets, documentation, and weekly demonstrations for business users. Work in sandbox and read-only environments with human approval required for live-system changes. The intern must be enrolled in a relevant U.S. degree program and available for continued part-time remote work after the summer.