Top Artificial Intelligence Jobs

Assesses and mitigates security risks across AI, machine learning, and Generative AI systems. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, governance, compliance, access controls, policy development, audit support, and advising engineering and business teams on secure AI development and responsible adoption.

Evaluates and validates AI chatbot functionality, response quality, conversational flows, compliance, and user experience. Executes test plans, regression testing, and user acceptance testing; documents defects and test outcomes; analyzes interaction trends; collaborates with AI trainers, prompt engineers, and development teams; prepares performance reports; and supports chatbot optimization and evaluation framework improvements.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Manage and optimize infrastructure supporting AI and machine learning systems, including GPU/CPU clusters, cloud environments, LLMs, data pipelines, and model deployments. Responsibilities include system monitoring, security, access management, automation, scripting, CI/CD support, troubleshooting, documentation, and performance optimization. The role collaborates with developers, data scientists, and prompt engineers to maintain reliable, scalable AI platforms.

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU and CPU allocation, security controls, monitoring, incident response, automation, and scalability. Collaborate with AI engineers, data scientists, and infrastructure teams; troubleshoot issues, perform root cause analysis, maintain documentation, evaluate emerging technologies, and mentor junior administrators.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Leads enterprise AI workflow automation strategy, identifying opportunities, prioritizing initiatives, designing scalable solutions, and managing implementation programs. Partners with business and technical stakeholders, oversees timelines and deliverables, establishes governance, measures ROI and operational outcomes, and drives continuous improvement. The role also evaluates emerging AI, RPA, and automation technologies while supporting organizational change, adoption, and transformation.

Designs, tests, optimizes, and manages prompts for large language models and AI applications. Develops reusable prompt templates, evaluates AI outputs, conducts A/B testing, monitors performance, and improves accuracy, consistency, and business relevance. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into prompting strategies. Researches generative AI advancements and contributes to prompt engineering standards, evaluation methods, and responsible AI practices.

Leads AI/ML engineering strategy, architecture, training, and implementation for an AI test laboratory and intern mentorship program. Builds production-grade data pipelines, MLOps workflows, model training and deployment systems, monitoring, explainability, and drift detection. Collaborates across technical, security, and stakeholder teams to deliver scalable AI capabilities, develop curricula, and ensure compliance with government requirements. The role also provides expert briefings, mentors junior personnel, and supports AI modernization across national security environments.

Leads the engineering team building and operating an enterprise AI platform. Responsibilities include platform reliability, security, scalability, cloud infrastructure, CI/CD, deployment automation, observability, governance, developer experience, technical standards, architecture reviews, and mentoring. The role requires production experience with AI or machine learning solutions, cloud-native platforms, distributed systems, infrastructure as code, containerization, platform security, and modern AI components such as vector databases, retrieval systems, orchestration frameworks, and model serving.

Define and govern enterprise AI architecture across machine learning, generative AI, automation, and agent-based capabilities. Develop standards, reference architectures, governance processes, and secure design patterns. Guide solution reviews, technical evaluations, production operations, and responsible AI practices while partnering with engineering, data, security, and business teams. Provide technical leadership, mentoring, and strategic direction for enterprise AI adoption.

Build lightweight AI prototypes and workflow solutions for portfolio companies. Identify operational bottlenecks, develop proof-of-concepts using large language models and emerging AI technologies, document technical requirements for engineering handoff, and collaborate with investment, management, and engineering teams to drive AI adoption, operational efficiency, and business ROI.

The Senior Machine Learning Engineer will develop NLP solutions, manage datasets, optimize performance metrics, and work on ML applications for healthcare.

Drive organization-wide adoption of AI development standards, tools, and workflows across engineering. Build feedback loops, enablement content, prompt libraries, playbooks, training assets, and adoption dashboards. Partner with engineering leaders to improve AI practices, support onboarding and office hours, measure productivity and quality impact, and communicate progress through technical narratives and success stories.

Design, develop, and deploy AI/ML applications for U.S. federal border security customers. Build optimized ML toolsets, maintain data governance, gather requirements from non-technical users, and support mission clients on-site in the Washington, D.C. metro area. Work includes cloud ML deployments (AWS/GCP) and configuring AI for law enforcement use cases; roles available at Associate, Senior, and Lead levels based on experience and capabilities.

Designs and leads end-to-end AI solutions across clinical, operational, and enterprise workflows. Partners with product, data science, engineering, cloud, and clinical teams to develop scalable architectures spanning data pipelines, models, MLOps, governance, monitoring, and EHR integration. Establishes standards for safety, reliability, observability, bias, and performance; guides build-versus-buy decisions and vendor integrations; creates reference architectures; and delivers measurable improvements in outcomes, efficiency, and user experience.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Perform analytic modeling, statistical analysis, and data engineering to extract value from large, messy datasets. Design and implement ML and inference methods, translate mission needs into technical requirements, and communicate technical findings and recommendations to non-technical stakeholders.

Provide cybersecurity consulting for clients including risk assessments, policy and disaster recovery planning, incident response, security awareness, regulatory IT examination support, AI integration guidance, vendor oversight, reporting to leadership and boards, project management, and IT audit assistance. Deliver tailored security programs, documentation, tabletop testing, and timely communications while adapting to evolving regulations and client needs.

Provide systems engineering technical advisory support to Intelligence Community ground enterprise programs. Design, develop, test, and integrate system architectures; manage requirements traceability, change/configuration, risk, readiness, verification/validation; analyze simulation data; support DevSecOps, Agile, MBSE, digital engineering, and deliver related training and roadmaps.

This 12-week AI engineering internship supports the development and implementation of generative AI solutions. Responsibilities include research, analysis, AI engineering projects, data processing, report generation, operational support, and collaboration across teams. Candidates will apply knowledge of Python, LLMs, prompt engineering, vector databases, RAG, APIs, data engineering, and cloud development while pursuing a relevant degree.

Build and operate end-to-end agentic AI products from initial concept through production and product-market fit. Responsibilities include developing agents, retrieval and orchestration systems, integrating tools, shaping product direction with design and product partners, hardening systems after launch, and creating reusable engineering primitives. The role requires entrepreneurial ownership, production AI experience, strong engineering judgment, and the ability to work effectively with technical and commercial stakeholders.

Translates business needs into AI solution requirements, user stories, acceptance criteria, and workflows. Coordinates Agile ceremonies, backlog quality, SDLC delivery, testing, stakeholder validation, risks, and releases. Supports AI product operations, evaluates AI outputs, develops training and documentation, manages the internal AI knowledge hub, and drives employee adoption of approved OpenAI and Anthropic capabilities. Promotes responsible AI, security, privacy, human oversight, and continuous improvement through feedback and usage insights.

Leads the architecture, development, and deployment of enterprise Generative AI and agentic systems for mortgage lending. Builds RAG pipelines, multimodal applications, cloud integrations, and LLMOps capabilities. Mentors engineers, guides technical strategy, evaluates emerging AI technologies, and collaborates with product, data, and UX teams to deliver scalable solutions. Requires strong software engineering or data science experience, expertise in LLM systems, cloud platforms, AI governance, model safety, and prompt engineering.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.