Top Artificial Intelligence Jobs

Design and implement machine learning research systems for generative models, causal inference, planning, and multi-objective optimization. Build training and inference pipelines, dataset loaders, experiment tracking, versioned datasets, and containerized model components. Develop synthetic data campaigns, translate research prototypes into tested production code, present findings, document results, and collaborate with RF, DSP, formal methods, academic, and government stakeholders.

Develop and sustain cloud-based software on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, architecture, Java development for distributed systems, testing, integration, performance tuning, CI/CD, monitoring, and operations. The role requires LLM and RAG expertise, infrastructure-as-code, microservices, Kubernetes, Linux, and scripting knowledge. The engineer will independently solve complex problems, balance cost and resilience, and guide junior team members.

Identify and evaluate enterprise AI and transformation opportunities, quantify business value, and translate business needs into responsible AI use cases. Develop proofs of concept, knowledge bases, and conversational AI agents; facilitate discovery workshops; collaborate with leadership, Technology, Risk, Compliance, and Operations; present recommendations; support adoption through training and communication; and contribute to AI governance, playbooks, and capability building.

Owns the enterprise architecture, implementation, governance, and evolution of Workday Talent, Performance, Skills, Learning, Recruiting, and AI capabilities. Leads requirements workshops, solution and integration design, Skills implementations, AI agent governance, release impact assessments, testing, roadmap development, and adoption metrics. Provides architectural guidance across global initiatives and partners with business, IT, and vendors to deliver secure, scalable, auditable Workday solutions.

Own the AI-powered content production engine for a healthcare website. Configure automation workflows, prompts, guardrails, templates, and HubSpot CMS publishing systems; oversee content quality, medical and legal reviews, compliance, SEO, analytics, vendor coordination, and web operations. Manage migrations, audits, redirects, performance monitoring, project workflows, and continuous process improvement across product, engineering, creative, medical, and legal teams.

Develops AI-assisted CAD/EDA automation, tools, and workflows for RTL design and verification. Responsibilities include troubleshooting EDA tools, building simulation and regression automation, improving CI/CD, analyzing infrastructure performance, supporting emulation and static sign-off, and collaborating with design, verification, infrastructure, cloud, and EDA vendor teams.

Design, develop, deploy, and maintain AI/ML models and pipelines. Collaborate with cross-functional teams, implement MLOps and CI/CD, monitor model performance, investigate incidents, ensure compliance with security/privacy/ethical requirements, and document model architectures and procedures.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Provides senior-level financial reporting and analytics support for the Utility business and Finance organization. Leads dashboards, KPI reporting, forecasting support, business reviews, analytical models, and leadership reporting. Uses business intelligence, automation, and AI-enabled tools to improve reporting efficiency and data quality. Partners across Finance, IT Data, operations, accounting, and commercial teams to standardize reporting, validate data, identify trends, and translate complex financial and operational analysis into actionable insights.

Develop and deploy AI agents using Gracenote MCP servers, lead technical pre-sales and proofs of concept, architect enterprise integrations, guide customer implementations, and advocate for product improvements. The role requires translating complex AI architectures into business solutions, supporting API integrations, and engaging stakeholders from executives to developers. Travel to customer sites globally may reach 40%.

Build and sustain BNY’s AI Risk program as part of the second line of defense. The role develops AI risk frameworks, governance routines, requirements, monitoring, reporting, and regulatory-aligned processes. It partners with Risk, Compliance, technology, and business stakeholders to identify and close gaps, manage the risk lifecycle, influence without direct authority, and communicate progress and risks to senior management. Candidates need at least six years of relevant AI, technology, cyber, data, model, or enterprise risk experience in a highly regulated industry.

Provide frontline technical support for an enterprise AI SaaS platform through email, chat, and ticketing systems. Diagnose issues, replicate bugs, query databases with SQL, investigate data discrepancies, and escalate complex problems. Educate nontechnical clients, manage cases in Salesforce and Zendesk, document solutions, optimize workflows, and share customer feedback with Product and Engineering. The role is suited to an early-career professional with a bachelor’s degree and interest in AI, SaaS, and technical product support.

Design, build, deploy, and operate production-grade AI and machine learning systems using Java, Python, and GCP. Develop scalable APIs, backend services, data pipelines, model-serving systems, and cloud-native platforms. Implement model lifecycle management, monitoring, testing, observability, and optimization. Collaborate with product managers, data scientists, engineers, and stakeholders to deliver secure, reliable AI solutions. Mentor engineers, participate in architecture and code reviews, and evaluate emerging AI technologies.

The Principal Engineering AI Solution Architect will lead AI initiatives, architect solutions for engineering challenges, and mentor teams on AI/ML integration and deployment.

Design, develop, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

Design, build, and deploy production-grade agentic AI systems, including autonomous and multi-agent architectures, tool-use orchestration, memory, retrieval, and function-calling workflows. Integrate agents with enterprise tools and databases, implement evaluations, guardrails, observability, fallback paths, and human-in-the-loop controls. Lead technical strategy, client collaboration, prototyping, and product direction for enterprise AI initiatives.

Leads global Applied AI Engineering programs from planning through rollout and ongoing operations. Manages milestones, dependencies, risks, regional execution, leadership updates, and performance tracking. Shapes industry strategy, adoption campaigns, technical workshops, partner initiatives, API demos, blueprints, and enablement playbooks. Improves internal operating mechanisms, workflows, tooling, dashboards, success metrics, and reporting while coordinating AIE, Sales, Product, engineering, leadership, and ecosystem partners.

Own bug triage, code fixes, testing, and product quality monitoring across AI-powered products. Interpret user feedback, identify meaningful issues, validate fixes and edge cases, communicate status clearly, and document recurring problems. The role requires independent judgment, product sense, technical troubleshooting, and effective use of Claude Code and other AI tools. It is fully remote for US or Canada-based independent contractors, with potential conversion to full-time employment.

Triages and resolves software product issues, including code fixes, testing, edge-case validation, and release verification. Interprets user feedback to identify root problems, monitors product quality, communicates issue status, and documents fixes and recurring patterns. Uses Claude Code and other AI tools to accelerate development while applying strong product judgment. This fully remote contract role supports US- or Canada-based candidates and may grow into product or client-facing work.

Triage and resolve product bugs by interpreting user feedback, working in the codebase, submitting and validating fixes, testing edge cases, monitoring product quality, communicating issue status, and documenting recurring problems. The role requires independent judgment, strong UX instincts, clear written communication, and effective use of Claude Code and other AI tools.

Triage and resolve product bugs, interpret user feedback, monitor product quality, test fixes and edge cases, and document recurring issues. The role requires working directly in the codebase, using Claude Code and other AI tools, communicating clearly with teammates, and independently prioritizing issues that affect user workflows. This fully remote contractor position is open to candidates in the US or Canada, with potential for full-time conversion.

Own product quality by triaging user-reported bugs, interpreting feedback, submitting code fixes, testing edge cases, validating workflows, monitoring systemic issues, documenting resolutions, and communicating status across teams. The role requires independent judgment, strong product instincts, clear written communication, and practical use of Claude Code and other AI tools. It is a fully remote contractor role for candidates in the US or Canada, with potential conversion to full-time employment.

Build and own a real-time conversational voice AI platform for automotive customers. Responsibilities include developing production systems across speech, LLM, telephony, and event-driven AWS services; optimizing latency and reliability; implementing observability, failover, infrastructure as code, CI/CD, and integrations; and leading technical decisions, engineering standards, knowledge sharing, and mentoring.

Leads independent second-line cybersecurity risk oversight, including governance, risk assessments, risk appetite, KRIs, issue remediation, regulatory engagement, emerging technology risk, and executive reporting. The role challenges cybersecurity strategies and controls, oversees AI and quantum computing risks, supports audits and regulatory examinations, advises senior leadership and boards, and manages a high-performing cybersecurity risk team.

Build and lead production AI infrastructure for institutional commercial real estate. Develop full-stack workflows, document parsers, data foundations, MCP servers, LLM features, evaluation systems, and React-based portals. Deploy solutions in client Azure environments, productize reusable components, ensure auditable accuracy, work directly with clients, and help hire and lead distributed engineers.