Top Artificial Intelligence Jobs

Provides technical leadership and architectural oversight for scalable AI-integrated software. Designs agentic AI systems, MCPs, APIs, RAG solutions, microservices, and prompt strategies; decomposes product goals into engineering tasks; reviews human- and AI-generated code; mentors distributed developers; and improves DevOps and CI/CD pipelines with AI-enhanced testing and reviews.

Designs and deploys AI-enabled decision workflows for Department of Defense and federal customers. Responsibilities include configuring data models, dashboards, what-if scenarios, AI agents, and automated workflows; integrating and transforming data; troubleshooting customer issues; advising end users; supporting adoption; and collaborating with product and development teams. The role combines solutions engineering, analytics, customer advisory, and agentic AI workflow design across enterprise platforms.

Join Arootah's advisor network to provide fractional Chief AI Officer / AI Strategy leadership to hedge funds, private equity firms, and family offices. Advise on AI initiatives, strategy, governance, vendor evaluation, workflow automation, change management, and implementation roadmaps. Engage on project-based assignments; client origination handled by Arootah.

Lead positioning, messaging, go-to-market strategy, launches, channel enablement, competitive intelligence, and analyst relations for Sophos’ AI cybersecurity solutions. Translate complex technical capabilities into buyer- and partner-focused narratives, build launch assets and sales tools, and coordinate cross-functional execution across product, sales, marketing, and research teams. The role also develops customer feedback loops, competitive battlecards, and category-defining market perspectives.

Define and govern enterprise AI and agentic security architecture. Build controls for agent permissions, tool access, inter-agent trust, LLM threats, AI asset visibility, Shadow AI discovery, and AI security posture management. Embed automated security benchmarks throughout AI development and deployment pipelines, establish enterprise standards, review AI designs, and partner with platform engineering, MLOps, DevOps, IAM, and architecture teams to enable secure, compliant AI adoption.

Design, build, and evaluate conversational AI agents using Go and Python. Own agent orchestration, sub-agent architecture, prompt engineering, tool integrations, guardrails, approval flows, observability, and evaluation infrastructure. Develop datasets, scorers, regression suites, and quality metrics for production readiness. Collaborate cross-functionally on conversational experiences, make architecture decisions, rapidly prototype agents, harden them for launch, and mentor engineers.

Architect and build backend systems powering conversational AI experiences. Lead complex projects across the software development lifecycle, define technical direction, design scalable solutions, and ensure system quality. Mentor engineers through code reviews, knowledge sharing, and onboarding while collaborating cross-functionally on product strategy, experiments, and high-value initiatives. Develop LLM-driven infrastructure and maintain operational excellence across production systems.

Lead the strategy and development of next-generation post-training systems for Waymo’s autonomous driving AI. Advance post-training techniques, research applications for real-world autonomous driving, build scalable metrics, data, and flywheels, and collaborate with ML platform and onboard software teams. The role also requires influencing engineering strategy and stakeholders across the organization, including director and VP-level leaders.

Lead the architecture and implementation of scalable conversational AI systems and backend services. Own projects from ideation through execution, shape the engineering roadmap, design RESTful APIs and business logic, integrate external partners, evaluate ML-driven results, and optimize platform scalability. The role also includes technical leadership, mentoring, code reviews, cross-functional collaboration, and product-focused decision-making.

Build Clarity’s product experiences end to end, collaborating with product design to create an intuitive user experience. Develop frontend infrastructure, abstractions, design systems, performance and monitoring capabilities. Own product improvements and make decisions amid ambiguity. Support customers, participate in sales calls and onboarding, and explore generative AI applications that solve customer problems.

Design and implement secure AI-powered tooling and platforms using LLMs, RAG, agents, and MCP. Lead architecture, prompt evaluation, safety guardrails, backend integrations, quality gates, monitoring, and performance optimization. Deliver Python services, mentor engineers, conduct code reviews, coordinate cross-functional development, lead incident analysis, and translate business strategy into engineering roadmaps and measurable priorities.

Lead the technical strategy, development, operation, and accreditation of a secure agentic AI platform in AWS GovCloud IL5. Build Kubernetes and Docker environments, CI/CD pipelines, Infrastructure as Code, observability, security controls, and MLOps capabilities. Develop ATO and cATO artifacts, integrate managed AI services, support container hardening and secure delivery, and collaborate with security and authorization stakeholders. Mentor engineers and guide the platform roadmap for operational DoD environments.

Designs, trains, evaluates, and deploys AI/ML and deep learning models for mission-critical applications. Analyzes complex datasets, develops predictive algorithms, integrates models into operational systems, and supports testing, validation, deployment, and lifecycle management. Collaborates with engineers, data scientists, and mission stakeholders; documents AI/ML initiatives; researches emerging technologies; and may support LLM, generative AI, NLP, cloud, distributed computing, and MLOps capabilities.

Designs, develops, and deploys governed AI and analytics solutions in sensitive mission-focused environments. Responsibilities include building AI workflows, applying analytic and AI governance frameworks, integrating APIs and cloud-native platforms, developing front-end experiences, analyzing disparate data, and delivering auditable models from concept through production. The role also requires collaboration with customers, agile delivery, and expertise in modern AI development, containerization, version control, and model management tools.

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Lead the creation and growth of a new industrial bottleneck intelligence company from validated concept through scale. Responsibilities include setting strategy and vision, developing go-to-market and operating models, recruiting the team, raising institutional capital, commercializing intelligence products, and scaling the venture with Interplay’s back-office support.

Research Staff will develop foundational voice AI technologies, including low-bitrate neural audio codecs, steerable speech generation, disentangled audio representations, latent recombination, synthetic audio data generation, and multimodal speech-to-speech models. The role also involves designing scalable architectures, training methods, and inference algorithms optimized for hardware, billion-hour datasets, and real-time deployment. Candidates need strong mathematical foundations, foundation-model expertise, large-scale data pipeline experience, rigorous experimentation skills, deployment optimization knowledge, and publications or open-source contributions in speech or language AI.

Develops and deploys AI, machine learning, generative AI, and predictive analytics solutions for DHS clients. Responsibilities include building data and AI pipelines with Python, SQL, Spark, Databricks, and AWS; developing LLM, RAG, NLP, computer vision, and predictive models; evaluating model performance and responsible AI practices; creating dashboards and documentation; and supporting MLOps, DevSecOps, deployment, governance, and technical mentoring.

Lead the strategy, architecture, and implementation of enterprise Azure data and AI solutions. Design scalable architectures using Microsoft Fabric, Synapse, Databricks, Azure databases, and AI services. Provide technical leadership, mentor engineering teams, oversee integrations, establish standards, and ensure security, governance, and operational excellence. Engage clients as a trusted technical advisor through workshops and executive presentations while partnering with project managers to deliver complex initiatives.

Build and operate AI-driven customer acquisition infrastructure, including outbound workflows, enrichment pipelines, lead routing, personalization, audience scoring, campaign automation, experimentation, and attribution dashboards. Partner with demand generation, lifecycle marketing, data analytics, product marketing, marketing operations, and sales to translate growth objectives into scalable automated systems.

Serves as the primary legal advisor for global data privacy, artificial intelligence, cybersecurity, information governance, and technology matters. Leads complex legal workstreams, develops governance frameworks, policies, playbooks, contractual standards, and compliance improvements, and advises senior leaders across jurisdictions. Partners with technology, security, regional, and global teams; monitors regulatory developments; manages external counsel; delivers risk-based advice, training, and practical guidance supporting responsible innovation and enterprise technology initiatives.

Lead technology development for next-generation glass manufacturing by designing advanced process control solutions, developing first-principles, system identification, data-driven, and machine learning models, and applying AI and advanced analytics to process monitoring, prediction, control, and optimization. Communicate technical findings through reports, presentations, and publications, and travel 10–15% to support project launches and execution.

Leads enterprise generative AI adoption by identifying use cases, designing secure AI-assisted workflows, developing prompts and reusable instructions, validating outputs, establishing human-in-the-loop controls, delivering role-based training, and monitoring adoption, productivity, quality, and risk. Partners with business, technology, security, privacy, compliance, legal, and data teams to support responsible AI use involving confidential information, PHI, and PII.

Drive enterprise sales and client adoption of Aera’s Decision Intelligence platform. Responsibilities include targeting and closing large enterprise clients, expanding existing accounts, managing C- and VP-level relationships, developing account strategies and transformation roadmaps, articulating technology value, collaborating cross-functionally, and exceeding customer revenue and ACV targets. Frequent travel and Texas residency are required.

Own product strategy and execution by translating customer needs and emerging AI capabilities into product bets. Lead engineering, design, growth, marketing, and sales from concept through launch and adoption. Set goals, measure outcomes, and improve each launch while shaping developer experiences for software validation.