Top AI Engineer Jobs

Develop secure generative AI services and workflow integrations for government modernization programs. Build retrieval-augmented generation, prompt orchestration, evaluation, monitoring, guardrails, access controls, audit logging, and human-approval workflows. Integrate AI services with Appian and authorized model endpoints, deploy through DevSecOps pipelines, document architectures and limitations, and coordinate defect resolution with technical, security, data, and product teams.

Build and maintain AI-powered healthcare products for patient engagement, care coordination, and claims management. Develop full-stack software, APIs, backend services, automation pipelines, and LLM integrations using Claude Code. Apply software engineering, DevOps, and MLOps practices for testing, deployment, monitoring, security, and performance. Collaborate with product, design, engineering, and clinical stakeholders in a remote Agile environment.

Design and own developer and test infrastructure for production-like environments. Build CI/CD pipelines, deployment automation, data-generation tools, workload simulations, and infrastructure supporting integration, load, performance, and correctness testing. Enable AI coding agents to test and validate code autonomously while improving engineering velocity, reliability, scalability, and cost efficiency. Collaborate with engineering teams to remove development bottlenecks and scale testing systems.

Leads software engineering efforts for secure, scalable banking technology. Designs, develops, tests, debugs, and reviews production code while guiding teams in responsible AI-assisted development. Applies Java, cloud, microservices, distributed systems, generative AI, performance testing, and chaos engineering practices. Evaluates technical solutions with vendors and internal teams, automates recurring remediation, improves operational stability, and leads engineering communities of practice.

Build AI-powered customer support workflows, tools, integrations, and knowledge systems. Investigate recurring issues, improve AI troubleshooting, automate repetitive support tasks, develop dashboards, and partner with Technical Support, Customer Success, and Product Engineering to improve customer experience and scalability.

Design, build, and operate large-scale production systems and integrate diverse technologies. Develop AI/ML-enabled end-to-end solutions on AWS, manage Kubernetes deployments, and implement observability. Lead technical initiatives, support policy/governance, and work within TS/SCI polygraph security requirements.

Build and operate production AI agent systems and full-stack features across a TypeScript monorepo. Responsibilities include database schemas, APIs, React components, agent infrastructure, telemetry, AI evaluation loops, monitoring, human review, safe fallbacks, and model fine-tuning. The role spans frontend, backend, data, infrastructure, and healthcare workflows in a regulated environment, with ownership of ambiguous problems from design through deployment.

Design, build, and maintain secure on‑prem and cloud infrastructure and CI/CD pipelines for Kubernetes-based services. Automate deployments, manage production/staging/dev environments, monitor logs and system health, enforce security controls, and support accreditation (A&A) and operational readiness. Troubleshoot incidents, perform root cause analysis, maintain documentation, and collaborate with Agile teams to drive DevSecOps best practices.

Define low-level technical requirements and validate AI-enabled features for an enterprise application. Work with customers, systems engineers, developers, and stakeholders to convert requirements into Jira user stories/epics, perform functional testing, and advise on AI approaches and integrations. Engage in Agile delivery and cloud-based deployments (AWS).

Build the execution layer of an AI-driven product, including persistent context, autonomous operation, evaluation systems, infrastructure, and user experiences across documents, messaging, and mobile. The role requires end-to-end architectural ownership, product and interface design instincts, familiarity with AI assistants, daily use of AI tooling, and experience creating measurement methods that influence team decisions.

Develop and integrate locally hosted LLM and ML capabilities into accredited, air-gapped government applications. Build data pipelines, REST API integrations, logging/audit instrumentation, and support deployment and monitoring in classified environments. Produce testable, documented code and collaborate with program stakeholders while following security and AI governance requirements.

Serves as the technical partner for AI alliance initiatives, evaluating partner technologies, designing integrations, and coordinating proofs of concept. Develops demos, solution architectures, sales materials, workshops, and training to drive internal adoption. Supports technical escalations, strategic accounts, proposals, co-selling, and partner engagement. Requires strong cloud, AI/data platform, API integration, solution architecture, communication, and public-sector mission experience. Hybrid role in the Washington, DC metro area requiring U.S. citizenship and ability to obtain a Secret clearance.

Designs and manages AWS cloud infrastructure and Amazon EKS platforms using GitLab CI/CD and Infrastructure as Code. Builds ETL workflows and scalable data pipelines for analytics and AI workloads, supports MLOps/AIOps and FinOps capabilities, troubleshoots Linux systems, and improves platform performance, security, scalability, and availability.

Develops predictive models and data-driven solutions by analyzing complex datasets and applying advanced statistical and machine learning techniques. The role communicates insights to stakeholders, collaborates with cross-functional teams, and integrates analytics into business strategy. It requires extensive data analysis experience, an active TS/SCI clearance with polygraph, and U.S. citizenship. The position is onsite in Herndon, Virginia, with less than 10% travel.

Develop and modernize secure, on-premises AI chatbot services for a classified government environment. Build Python microservices, APIs, RAG pipelines, document-processing workflows, and LLM inference integrations using technologies such as OpenWebUI, vLLM, Ollama, PostgreSQL, and pgvector. Containerize and deploy services in air-gapped environments, implement testing and DevSecOps practices, support agentic AI capabilities, troubleshoot distributed systems, and collaborate with AI, infrastructure, cybersecurity, and government stakeholders.

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.

Design, develop, deploy, and sustain AI-enabled web applications, analytics interfaces, APIs, workflow integrations, and data-layer components. Build LLM-powered agentic workflows using prompting, tool calling, RAG, structured outputs, and multi-step execution. Develop Python backends, React or Streamlit interfaces, relational database integrations, CI/CD pipelines, and Docker-based deployments. Collaborate across the software development lifecycle while supporting secure, mission-focused AI and analytics capabilities.

Lead and sustain large-scale HPC systems supporting NOAA/NWS forecasting. Manage scheduler/software stack (PBS Pro/Slurm), optimize performance and throughput, troubleshoot multi-node Linux clusters, develop scripts, and support 24/7 operations with occasional travel and on-call duties.

Performs hands-on cybersecurity and AI engineering, defines and documents functional and technical requirements, converts requirements into Jira stories, validates application features, and collaborates with customers, developers, systems engineers, and AI stakeholders. Evaluates AI architectures, model performance, integration tradeoffs, and emerging AI/ML applications for enterprise software. Requires active TS/SCI with polygraph clearance, U.S. citizenship, a bachelor’s degree, and 15+ years of experience.

Design, build, deploy, and sustain AI-enabled full-stack applications, analytics interfaces, APIs, data layers, and agentic workflows. Develop Python services, React or Streamlit interfaces, FastAPI integrations, LLM features, CI/CD pipelines, and Docker-based deployments. Work with relational databases, Git, and secure software delivery practices while collaborating across the software development lifecycle. The role requires a current TS/SCI clearance, ability to obtain a CI polygraph, eight or more years of experience, U.S. citizenship, and onsite work in Crystal City, Virginia.

Develop and maintain scalable full-stack web applications for an advertising measurement and reporting platform. Build responsive interfaces, APIs, backend services, microservices, database integrations, and AI agent solutions. Optimize performance, scalability, reliability, and data integrity; troubleshoot complex issues; write automated tests; conduct code reviews; mentor junior engineers; and collaborate with cross-functional teams and contractors throughout the development lifecycle.

Build and ship production full-stack applications powered by AI and data. Responsibilities include client discovery, technical architecture, LLM and agentic workflow implementation, MLOps/LLMOps, testing, monitoring, scalable data pipelines, data lakes, and transparent communication of technical tradeoffs. The role requires adapting across technologies and industries while delivering tailored solutions in a consulting environment.

Develop full-stack, infrastructure, robotics, and graphics products for autonomy development workflows. Engineers build features and tools across the technology stack, collaborate with domain experts and users, influence product decisions, and solve complex engineering problems in a fast-paced environment.

Build and integrate AI-powered internal tools using Claude, Claude Code, Python, and JavaScript or TypeScript. Identify workflow automation opportunities, maintain prompts and AI skills, connect AI to internal systems through APIs, track tool impact, document solutions, deliver demos and training, and coordinate security and compliance for data handling and access controls.

Design, train, evaluate, and deploy production machine learning, deep learning, and reinforcement learning systems for semiconductor manufacturing. Build RL environments, reward functions, policies, agents, simulation workflows, and scalable MLOps pipelines. Research generative AI, LLMs, RAG, and multi-agent systems while partnering with software engineers, data scientists, cloud architects, and semiconductor experts to integrate intelligent capabilities into production solutions.