Top Artificial Intelligence Jobs

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Leads the design and development of AI-driven enterprise and cloud security solutions. Manages teams delivering data, analytics, and machine learning engineering projects; oversees AI model implementation, data infrastructure, pipelines, and platform deployments. Uses Python and C++ for algorithm development, analyzes data, improves data quality, ensures technical compliance, mentors staff, manages client expectations, and drives innovation across cybersecurity and privacy initiatives.

Lead the development and production deployment of machine learning, generative AI, and multi-agent solutions. Translate business problems into scalable AI products, build models and data pipelines, architect cloud and lakehouse systems, and implement MLOps, LLMOps, evaluation, governance, security, and monitoring. Partner with cross-functional stakeholders, communicate technical outcomes, troubleshoot production issues, mentor teammates, and shape the strategic roadmap for enterprise AI capabilities across Azure, Databricks, AWS, and GCP.

Leads a Gen AI engineering team developing intelligent agents, digital assistants, and AI-driven customer experiences. Provides technical direction, manages resources, oversees team performance and development, establishes engineering best practices, and owns product quality. Requires strong software engineering, integration, testing, organizational leadership, program management, and cross-functional project delivery experience in agile, matrixed environments.

Architects, builds, and operates production-grade agentic AI systems for identity security at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, failure recovery, enterprise-grounded reasoning, retrieval and RAG pipelines, model integration and evaluation, safety guardrails, observability, and human-in-the-loop controls. Provides technical leadership through architecture ownership, code reviews, quality standards, and mentoring.

Develop and test AI software using large datasets, Python, JavaScript, and C++. Evaluate algorithms, statistics, and data to improve industrial applications. Collaborate with machine learning and data engineers on end-to-end AI and integration solutions, stay current with AI trends and regulations, and present project progress to leadership and stakeholders.

Designs and executes strategic, operational, and tactical wargames supporting U.S. Army training, experimentation, capability development, and analysis. Develops game mechanics, adjudication systems, decision models, and adversary behaviors grounded in military doctrine and threat capabilities. Applies game theory, operational analysis, and data-driven evaluation to produce recommendations for senior leaders. Leads workshops and tabletop exercises while collaborating with developers, data scientists, and AI teams to integrate wargames into simulation environments.

Drives strategic revenue growth across key accounts by building CXO-level relationships, applying consultative enterprise sales, identifying expansion opportunities, managing complex negotiations, forecasting pipeline, and partnering with pre-sales, specialists, customer success, and ecosystem partners. The role aligns SUSE’s Linux, Kubernetes, cloud, edge, AI, and security solutions with customer objectives while pursuing market share and long-term account value.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

Lead end-to-end AI and data science initiatives across Alcoa’s Operations and Enterprise functions. Develop, validate, deploy, and continuously improve statistical, machine learning, generative AI, RAG, and agentic AI solutions. Translate business challenges into scalable models, evaluate outcomes, and deliver measurable value. Provide technical leadership, mentor data scientists, and collaborate with engineers, architects, analysts, and stakeholders to build trusted, responsible AI capabilities.

Lead exploratory AI research and innovation initiatives, evaluating emerging techniques such as neuro-symbolic AI, causal AI, agentic AI, reasoning systems, and knowledge-enhanced machine learning. Design prototypes, assess capabilities and risks, identify business applications, and collaborate with engineering teams to transition successful concepts into production. Represent TransUnion through publications, patents, and presentations while shaping long-term AI strategy.

Lead go-to-market strategy and product marketing for Arm’s Cloud AI portfolio, including AGI CPUs, Neoverse CSS, CPU IP, platforms, and ecosystem solutions. Develop positioning, messaging, customer value propositions, marketing plans, product launches, partner campaigns, content, sales enablement, and executive communications. Collaborate with product, engineering, sales, corporate marketing, and ecosystem partners to translate complex AI and cloud infrastructure technologies into differentiated market narratives and accelerate adoption.

Own the end-to-end product direction for Brex’s Finance Admin AI agents, including strategy, roadmap, agent behavior, prompting, evaluation, and quality. Partner with engineering, design, research, and finance administrators to build trustworthy agents for expense auditing and business analysis. Translate complex finance workflows into production capabilities, prototype and analyze agent performance, establish feedback loops, and determine when agents should recommend or act autonomously.

Lead advisory engagements with financial institutions from pre-sale through expansion. Understand bankers’ and investors’ workflow challenges, demonstrate tailored AI solutions, build executive relationships, and establish credibility across financial services use cases including research, M&A, and diligence. Partner with marketing on industry content, serve as the voice of the customer for sales and product teams, tailor new solution introductions, and conduct customer and competitor analysis.

Leads the portfolio marketing operating model across Neurology and Rheumatology, prioritizing resources across brands, launches, and lifecycle needs. Translates therapeutic and indication strategies into integrated marketing activation, omnichannel engagement, congress execution, performance optimization, and measurable outcomes. Partners with senior cross-functional stakeholders, agencies, and vendors while building scalable capabilities, AI-enabled practices, agile ways of working, and a high-performing marketing team.

Lead HCP commercial strategy for emerging biotech pipeline assets, future indications, partnerships, and strategic opportunities. Develop customer ecosystem and KOL strategies, assess commercial readiness, guide launch and transition planning, and integrate scientific, market, and customer insights into investment decisions. Partner across Medical Affairs, Clinical Development, Business Development, Market Access, Insights, and commercial teams. Lead and develop teams while building capabilities in early commercialization, pipeline prioritization, ecosystem development, and launch readiness.

Design, build, and scale backend systems and microservices for automated chargeback and dispute workflows. Integrate with card networks, banks, and merchants, improve CI/CD, upgrade platform architecture, and own feature delivery and system optimizations.

The Data Scientist develops advanced analytics, AI, machine learning, predictive models, and business intelligence solutions supporting pharmaceutical commercial strategies. Responsibilities include analyzing complex healthcare and longitudinal patient data, building scalable algorithms and automated analytics capabilities, integrating and governing cloud data, and communicating insights to technical and non-technical stakeholders. The role partners cross-functionally to support market intelligence, customer segmentation, targeting, promotional measurement, and data-driven decision-making across oncology, rare disease, and CNS franchises.

Manage enterprise customer accounts throughout the sales lifecycle, from pipeline generation through deployment, expansion, and renewal. Develop account strategies, own revenue targets, forecast revenue, and analyze account metrics. Partner with engineering, solutions, research, marketing, and product teams on complex customer programs. Engage C-level executives, communicate technical AI concepts, monitor market intelligence, support recruiting, and help build scalable company processes and culture.

Leads engineering teams building responsible, scalable AI systems and products. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, observability, and production deployment. Makes build-versus-buy decisions across AI technologies, develops LLM optimization techniques, shapes foundational AI roadmaps, and recruits and mentors engineering talent. Partners with research, engineering, product, and program teams to deliver high-performance AI capabilities.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Leads enterprise data and AI engineering strategy across business units. Responsibilities include modernizing cloud data platforms, establishing governance and semantic layers, enabling self-service and AI-powered analytics, building reusable data products and models, and supporting machine learning, generative AI, RAG, and agentic AI architectures. The role manages data engineering, architecture, AI engineering, and analytics enablement teams while partnering with executives and business stakeholders to deliver trusted, scalable data capabilities.

Leads the planning, design, specification, deployment, and scaling of advanced electrical, instrumentation, and automated test systems across manufacturing and laboratory environments. Coordinates equipment teams and test platform developers, evaluates sensors and technologies, supports NPI and DFMA/DFT initiatives, and converts test data into operational insights. Provides technical leadership, mentoring, troubleshooting, procurement support, and cross-functional program ownership while applying networking, software, data analysis, lean manufacturing, and Industry 4.0 technologies.

Manage the post-sale lifecycle for strategic platform partners, driving adoption, retention, expansion, and value realization. Coordinate API/SDK solution development with customer and internal engineering teams, lead executive business reviews, analyze performance metrics, and support partner enablement through training, collateral, and success stories. Improve customer success processes, collaborate with Marketing on engagement, and provide insights to Product. The role requires enterprise relationship management, technical understanding, and flexible scheduling across U.S. time zones.