Top Artificial Intelligence Jobs

Leads architecture, deployment, monitoring, automation, governance, and lifecycle management for production machine learning systems. Manages datasets, ML pipelines, model registries, evaluation, drift monitoring, testing, rollback, security, compliance, and audit documentation. Partners with data, cloud, software, cybersecurity, testing, operations, and government stakeholders to deliver secure, traceable, mission-critical ML capabilities.

Build and operate production machine learning capabilities, including dataset management, automated training and deployment pipelines, model monitoring, drift detection, governance, testing, rollback, and secure cloud integrations. Collaborate with data, software, cloud, cybersecurity, testing, operations, and government teams. Maintain traceability, documentation, auditability, incident response, and human-review controls across the model lifecycle.

Designs and secures AI and cloud solutions for government operations, including knowledge assistants, retrieval workflows, bounded agents, APIs, integrations, authentication, authorization, audit logging, testing, monitoring, and human-approval controls. Translates mission requirements into scalable architectures and supports documentation, security compliance, troubleshooting, deployment, and technical guidance. Requires active Secret clearance, a relevant bachelor’s degree, and at least 10 years of experience.

Build AI and machine learning systems for analog and mixed-signal IC design, including structured artifact modeling, design-space optimization, physics-informed surrogate models, scalable data and compute infrastructure, and agentic engineering tools. The role requires deep expertise in machine learning, large-scale systems, applied mathematics, optimization, or circuit design, plus strong Python software development skills and the ability to translate research into reliable, reproducible systems.

Designs and executes functional, regression, integration, automated, voice, and end-to-end testing for conversational AI, IVR, and customer engagement platforms. Validates intents, entities, utterances, speech recognition, call routing, integrations, APIs, and customer journeys. Creates test plans, cases, automation suites, reports, and quality metrics; tracks defects and collaborates with technical and product teams. Requires extensive Cyara experience, conversational AI platform testing, IVR validation, and strong QA methodologies.

Design and validate mixed-signal hardware, power electronics, and high-speed digital systems. Perform circuit, sensitivity, and signal-integrity analysis; support PCB design, board bring-up, and testing. Develop embedded firmware for control and power regulation systems, while integrating AI tools to automate engineering workflows and analysis. Serve as a technical liaison between IT infrastructure and design engineering teams.

Lead and execute high-impact AI initiatives, serving as a technical authority on agentic systems and fullstack development, while mentoring and optimizing infrastructure strategy.

Own end-to-end dealership implementations for AI products, including discovery, configuration, data validation, go-live, hypercare, troubleshooting, training, and change management. Manage project timelines and stakeholders, diagnose logs and integration issues, escalate product bugs, create runbooks and SOPs, and relay field feedback to product and engineering teams. The role is customer-facing, hybrid, and requires 50–70% travel to client dealerships.

Lead and scale engineering teams to design, build, and operate enterprise-scale AI-powered software and platforms. Define technical strategy, establish engineering best practices and architecture governance, partner with Product and Security, deliver secure, resilient cloud-native solutions, and recruit and develop engineering leadership to drive measurable business value through Generative AI and agentic technologies.

Lead architecture and hands-on development for scalable GCP infrastructure and AI platforms. Build Python microservices, deterministic policy engines, governed data pipelines, GraphRAG retrieval, Vertex AI agent workflows, and evaluation systems. Establish data contracts, security controls, observability, CI/CD, and cost-efficient cloud operations while mentoring engineering squads and translating enterprise architecture into delivery plans.

Support early-career AI/ML development for Intelligence Community missions by prototyping GenAI, LLM, NLP, and computer vision solutions. Responsibilities include configuring development environments, containerizing applications with Docker, integrating prototypes into CI/CD pipelines, deploying to AWS or Azure secure environments, preparing customer demonstrations, and documenting APIs and systems. The role requires hybrid work in Northern Virginia, including classified SCIF and customer facilities.

Design and build the production foundation for WGU’s AI student assistant, including retrieval, inference, conversation state, security, privacy, guardrails, evaluation, observability, and service integration. Develop full-stack features in Java and Angular, integrate enterprise systems, support production operations, and mentor engineers. The role requires hands-on LLM application development, authorization-aware data access, and technical leadership across security, reliability, scalability, and cost.

Owns product strategy, roadmap, and execution for AI-powered experiences serving home service professionals. Responsibilities include researching workflows, defining requirements, shaping model behavior and user experiences, prioritizing roadmaps, establishing product and AI performance metrics, coordinating launches, and improving products through customer feedback and analytics. The role partners closely with Engineering, Design, Data, Marketing, Sales, Customer Success, Operations, and AI platform teams.

Develop and productionize large language models and agentic AI systems at scale. Design APIs, libraries, container strategies, and LLMOps infrastructure; optimize deep learning training and inference on specialized hardware; and lead AI projects from research and data exploration through implementation and productization. Collaborate with research scientists, engineers, and data scientists across multimodal AI, NLP, computer vision, knowledge representation, and generative AI.

Own customer relationships and deployment journeys for physics AI solutions. Translate engineering challenges into product opportunities, guide onboarding and change management, coordinate delivery teams, support roadmap and sprint planning, track adoption and value, and identify expansion opportunities. Prepare proposals and support commercial contracting while collaborating with simulation, software, machine learning, and data science teams.

Lead enterprise AI architecture strategy, adoption roadmaps, standards, governance, and responsible AI practices. Partner with technical and business leaders to identify AI opportunities, evaluate platforms, design scalable solutions, and guide cross-functional teams in AI-enabled development, automation, and workflow adoption. Coach architects and developers while ensuring solutions are secure, ethical, compliant, and aligned with business objectives.

Designs, develops, deploys, and operates intelligent automation solutions using AI/ML, open-source LLMs, and MCP integrations. Builds document-processing and data-cleansing bots, agentic workflows, and reusable automation components. Owns MLOps pipelines, model retraining, governance, token optimization, monitoring, troubleshooting, and performance tuning. Partners with stakeholders to identify automation opportunities, ensures security and compliance, and evaluates emerging models and frameworks.

Designs, develops, deploys, and supports full-stack intelligent applications integrating AI/ML models with business workflows. Builds reusable document processing, data extraction, classification, and data cleansing solutions; develops MCP servers and tools; manages model deployment, monitoring, retraining, and optimization. Troubleshoots production issues, performs root cause analysis, maintains operational documentation, and collaborates with business, data, infrastructure, security, and platform teams.

Leads the design, implementation, administration, and support of enterprise Kong AI Gateway solutions. Develops secure LLM proxies, integrates multiple AI providers, configures guardrails and governance policies, and implements routing, observability, token analytics, cost controls, and RAG integrations. Manages Kong in Kubernetes environments, automates deployments, monitors performance, and drives platform reliability, scalability, compliance, and enterprise GenAI adoption.

Build and deploy enterprise AI agents, assistants, and automated workflows that improve internal business processes. Partner with stakeholders to identify opportunities, integrate AI solutions with enterprise systems and data, and develop secure retrieval and grounding capabilities. Responsibilities include production deployment, testing, monitoring, observability, access controls, evaluation, cost management, and measuring business impact. The role also develops reusable AI components and promotes responsible enterprise AI adoption.

Leads AI and automation strategy across Finance, including machine learning proof-of-concepts, model review, MLOps deployment, adoption, training, governance, and enterprise scaling. Manages and mentors data scientists, partners with technology teams, develops AI roadmaps, supports data architecture, and drives change management and stakeholder adoption. The role requires strong financial acumen, hands-on machine learning and MLOps experience, enterprise data strategy knowledge, and at least eight years of progressive experience.

Lead hands-on development and production deployment of machine learning, large language model, and generative AI solutions. Set technical direction, make architecture decisions, establish MLOps pipelines, monitor model performance, optimize and fine-tune generative models for NLP, and ensure reliability and scalability. Partner with product, data science, engineering, and business stakeholders, communicate technical results clearly, mentor engineers, and guide delivery across multiple workstreams.

Design and implement AI/ML solutions for cyber and intelligence workflows, including LLM-powered workflows, agent-based automation, and API or enterprise data integrations. Partner with mission users, analysts, developers, and leadership to document operational processes, build reliable production capabilities, and meet classified-environment security and governance requirements.

Summer interns will build and test AI solutions, LLM applications, NLP tools, machine learning systems, data pipelines, and semantic data models. Responsibilities include evaluating AI models, automating workflows, applying software and data engineering practices, and collaborating with scientists and business teams. Interns will use responsible AI practices and contribute to real-world systems supporting medicine development.

Leads client-facing AI discovery, advisory, and solution design for legal departments. Translates business priorities into AI use cases, requirements, implementation plans, governance approaches, and value cases. Partners with sales, product, technology, delivery, legal talent, and commercial teams on proposals, pilots, managed solutions, and strategic adoption projects. Advises clients on AI transformation, policy, risk, security, vendor selection, rollout, and success measurement while developing thought leadership and repeatable solution frameworks.