Top Artificial Intelligence Jobs

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.

Own Turing’s AI ecosystem partner channel, driving partner-sourced revenue, co-selling, referrals, pipeline, and enterprise account planning. Manage partner programs including onboarding, deal registration, certifications, tiers, funding, training, reporting, business reviews, and executive engagement. Coordinate competing technology partners fairly, ensure delivery commitments are staffed, and collaborate across sales, delivery, and industry teams. Build case studies, grow channel revenue, and potentially develop a partner team.

Design and develop generative AI and LLM solutions on Google Cloud and Vertex AI. Build data pipelines, RAG and prompt-engineering strategies, and integrate AI capabilities into dashboards, APIs, and real-time analytics. Work with structured and unstructured data, collaborate with engineering teams on deployment, monitor advancements in generative AI, and communicate technical findings to technical and non-technical audiences.

Designs end-to-end AI solutions aligned with customer outcomes and Presidio capabilities. Partners with sales, engineering, product, and customers on discovery, proposals, RFPs, demonstrations, architectural reviews, and technical feasibility. Creates solution diagrams, reference architectures, and technical documentation; advises customers and supports adoption. The role also mentors junior technical staff, contributes to architecture best practices, and stays current on AI/ML, cloud, data platforms, and modern application architectures.

Lead the technical vision, architecture, roadmap, and implementation of Intel’s neuromorphic AI runtime stack. Own runtime performance, real-time behavior, production quality, compiler and framework interfaces, and firmware and driver execution contracts. Collaborate across compiler, hardware, application, customer, and partner teams to optimize workloads across silicon and simulators. Provide technical leadership, establish engineering practices, mentor engineers, and guide commercialization of event-driven AI technology for edge and robotic systems.

Build and operate the platform supporting internal applications, dashboards, AI agents, data pipelines, and CI/CD. Manage Azure hosting, networking, identity, secrets, monitoring, access controls, infrastructure as code, and deployment standards. Integrate Microsoft Fabric with ERP, CRM, telematics, and other systems. Establish guardrails for AI-assisted development, automate workflows, monitor cost and reliability, and create repeatable engineering patterns in partnership with IT.

Take AI-generated prototypes into production by refactoring, designing architectures, building internal web applications and integrations, creating data models, writing tests and documentation, and instrumenting deployed tools. Direct AI coding tools, review analyst code, ensure security and maintainability, reconcile data across ERP, CRM, telematics, and other systems, and support technicians, schedulers, buyers, and planners.

Lead and coach Revenue Operations and Sales Operations teams while owning Athena’s revenue technology stack. Design and administer HubSpot, billing and payment integrations, lead routing, enrichment, referrals, checkout automation, reporting, and data quality. Build AI-driven sales workflows and automation tied to revenue outcomes. Partner with Sales, Finance, Legal, InfoSec, Tech, Marketing, and Operations on systems, forecasting, handoffs, vendor decisions, and process improvements.

Build functional, testable AI-powered prototypes for mobile and web customer experiences. Research and integrate LLMs, conversational AI, recommendations, personalization, generative content, voice capabilities, APIs, cloud services, and customer data. Independently turn ambiguous concepts into proof-of-concepts, create reusable components, apply guardrails, and iterate based on customer and user feedback. Collaborate with marketing and design while using AI-native development tools to rapidly explore and validate new experiences.

Lead and grow Tempus partnerships with biopharma clients by identifying prospects, conducting executive outreach, developing global account strategies, supporting contract negotiations, aligning cross-functional teams, and delivering revenue and strategic objectives. Travel ~20% and provide client feedback to inform product portfolio.

Leads AI and machine learning strategy across a six-task-order portfolio. Architects, deploys, and optimizes production AI/ML solutions; establishes model lifecycle, MLOps, DevSecOps, and governance practices; modernizes legacy systems; evaluates emerging technologies; manages technical risks, cost, schedule, and performance; and mentors teams while engaging technical and customer stakeholders.

Support AI/ML engineers in developing generative AI and LLM applications, including chatbots, RAG pipelines, prompt engineering, model evaluation, data preprocessing, and data pipelines. Learn and apply deep learning concepts, research emerging techniques, explore vector embeddings, and collaborate on architecture and implementation tasks under senior engineer guidance.