Top Artificial Intelligence Jobs

Architect and deploy enterprise LLMs, autonomous multi-agent systems, and real-time AI solutions for supply chain operations. Build secure cloud infrastructure, event-driven pipelines, evaluation frameworks, data integrations, and proactive automation. Lead architectural decisions, establish LLMOps and MLOps standards, mentor engineers, and collaborate with business leaders to deliver production AI products.

Owns system architecture for AI accelerator rack- and cluster-scale platforms. Defines requirements, specifications, and proof-of-concepts; evaluates architecture options using performance simulations and hardware models; makes tradeoffs across performance, total cost, networking, memory, storage, power, cooling, and datacenter constraints. Drives alignment among engineering and leadership teams, influences vendor roadmaps, and participates in industry standards development.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Leads Prudential’s enterprise AI workforce transformation strategy, including AI-enabled job redesign, human-AI teaming, organizational models, governance, talent architecture, career paths, change management, and global workforce planning. Partners with technology and people executives to guide agentic AI adoption, develop frameworks and playbooks, oversee responsible AI practices, measure productivity and ROI, and build organizational capabilities for AI-augmented work across multiple geographies.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Own and evolve patient-facing clinical application surfaces, including a React widget, clinician portal, and Python service layer. Lead full-stack architecture, AI application integrations, encounter data flows, internal tooling, versioned shared components, and operational reliability. Partner with AI engineers on LLM-enabled workflows, maintain dependencies, protect consumers from breaking changes, and mentor junior engineers. The role requires durable ownership of rapidly built systems and delivery of critical healthcare technology.

Analyze complex datasets using advanced SQL to measure expert performance, task quality, throughput, and operational efficiency. Build metrics, dashboards, and self-service analytics; identify data quality issues; and partner with Product, Operations, and other stakeholders to guide workflow improvements and business decisions. Translate ambiguous questions into actionable analyses and communicate findings to technical and non-technical audiences.

Develop and maintain MDLinx software features, contribute to system design and code reviews, apply testing and software engineering practices, and troubleshoot production issues. The role emphasizes critically reviewing AI-generated code, identifying opportunities for AI-assisted automation, and supporting application performance, security, and compliance. Engineers collaborate across technical and business teams while expanding ownership and learning the pharmaceutical and media domains.

Own and evolve finance systems and the financial planning platform to automate and scale planning. Lead systems roadmap, integrations, AI adoption, dashboards, data architecture, and cross-functional partnerships to improve forecasting, controls, and decision-making.

Leads enterprise-wide AI and engineering transformation as a distinguished technical individual contributor. Drives adoption of AI-powered product capabilities, agentic customer experiences, AI-assisted development, and modern engineering practices. Provides architectural leadership, evaluates emerging technologies, mentors teams, aligns senior stakeholders, and leads complex initiatives through influence across platform, product, AI/ML, and application engineering organizations.

Leads the design, development, and deployment of AI solutions for finance, controllership, and FP&A functions. Responsibilities include building predictive and generative models, architecting data pipelines, managing client engagements, supervising teams, controlling budgets and timelines, translating technical outputs for finance stakeholders, supporting proposals and business development, and maintaining client relationships. The role requires finance expertise, AI engineering experience, consulting skills, and travel of up to 30%.

Lead architecture and implementation for reliable, scalable AI authoring platforms. Build cloud infrastructure, CI/CD pipelines, Kubernetes and Helm deployments, infrastructure-as-code, observability, disaster recovery, A/B testing, and release processes. Improve backend reliability, performance, security, and cost efficiency across Azure and Google Cloud. Partner with product and engineering teams on technical strategy, ML infrastructure, LLM inference, and AI-assisted game development tools.

Advises enterprise CISOs and SOC leaders on security transformation, operating models, architecture, adoption, and measurable outcomes. Leads strategic pre-sales engagements, executive workshops, solution scoping, and post-sales journey assurance across MDR, SOC transformation, automated SOC, and SIEM/SOAR displacement motions. Develops reusable playbooks and transformation frameworks, mentors solution engineers, identifies expansion opportunities, and translates customer needs into product, engineering, services, and roadmap input.

Architect and govern production-grade generative AI solutions across enterprise platforms. Design integrations involving large language models, retrieval-augmented generation, prompt engineering, autonomous agents, microservices, APIs, data, and cloud technologies. Establish architectural standards, evaluate AI platforms and toolchains, optimize scalability and security, and lead technical design reviews. Requires extensive enterprise architecture experience, strong communication and prioritization skills, cross-functional collaboration, and the ability to perform under pressure. Banking experience is preferred.

Build and deploy production-grade Generative AI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating enterprise data and applications; deploying and scaling cloud-based systems; and optimizing reliability, security, performance, and cost. The role requires direct client engagement, strong software engineering skills, cloud expertise, and hands-on experience with LLM frameworks, agent orchestration, and production AI operations.

Develop and operationalize AI/ML capabilities for secure Department of Defense mission systems. Responsibilities include building agentic workflows, production APIs, MLOps pipelines, model validation and monitoring processes, and integrations with MSS Foundry and Databricks. The role requires production AI/ML deployment experience, Python proficiency, lifecycle management expertise, and collaboration with engineering and mission teams. Candidates must hold an active TS/SCI clearance and have at least five years of relevant experience.

Partner with key business customers to optimize DigitalOcean cloud deployments, design architectures, troubleshoot technical challenges, drive retention and expansion, conduct business reviews, advocate for customer needs, and collaborate cross-functionally. The role also involves creating automation tools, delivering technical presentations and training, monitoring usage trends, and advising customers on cloud, AI/ML, cost, and performance optimization.

Leads the design, development, deployment, and delivery of AI-enabled finance solutions for Controllership and FP&A clients. Responsibilities include developing predictive and generative AI models, architecting data pipelines, translating technical outputs into business insights, managing budgets and timelines, supervising teams, supporting proposals and business development, and maintaining client relationships. The role requires finance expertise, AI engineering experience, client advisory skills, and up to 30% travel.

Lead Azure sales and pre-sales strategy for strategic Oil & Energy accounts, driving pipeline, consumption growth, and C-level relationships. Translate industry insights into cloud and AI solutions, orchestrate partners and internal teams, lead POCs/ pilots, mentor junior staff, and promote Azure AI/analytics modernization to deliver measurable business value.

Leads Vertex’s ICX data, analytics, AI, decisioning, and agentic strategy. Oversees data products, predictive models, AI agents, measurement, governance, Responsible AI, vendor partnerships, and commercial value realization. Partners with technology, commercial, legal, medical, regulatory, privacy, and compliance stakeholders while building and developing a multidisciplinary team of approximately nine direct reports.

Own the production lifecycle of AI capabilities, including deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration. Design reliable online and batch inference systems while partnering with ML, data, product, and engineering teams. Lead MLOps and distributed-systems initiatives, improve observability and operational resilience, support incident response, mentor engineers, and ensure secure, privacy-conscious handling of healthcare data.

Build AI-enabled applications, automations, integrations, data workflows, and prototypes across Salesforce, MuleSoft, .NET, RPA, and enterprise platforms. Partner with business and IT stakeholders to identify opportunities, validate measurable value, assess AI and architectural risks, and deliver supportable solutions. Document designs, dependencies, prompts, limitations, runbooks, and handoffs while contributing to exploratory engagements and recommending pivots when initiatives lack value.

Own the architecture and technical delivery of an internal AI orchestration platform. Build infrastructure, shared libraries, developer tools, agent frameworks, and secure automated execution environments that enable feature teams to integrate agentic AI. Optimize LLM interactions, prompts, token usage, embedding stores, testing, deployment pipelines, and platform architecture. Partner across UI, API, and data layers while mentoring teams and operating within an Agile environment.

Creates, edits, and optimizes scientific and marketing content for digital channels. Conducts keyword, topic, and user-intent research; applies SEO and AEO practices; improves content structure, taxonomy, internal linking, and discoverability. Reviews existing content for gaps and duplication, analyzes performance data, collaborates with SEO, marketing, and digital teams, participates in peer reviews, and evaluates AI-assisted content for scientific accuracy and quality.

Improve Renesas content visibility across traditional search, AI answer engines, and onsite search. Responsibilities include SEO and GEO strategy, search performance reporting, onsite search analysis, technical optimization, content recommendations, stakeholder presentations, and partnership with web, product, engineering, analytics, and marketing teams.