Top Artificial Intelligence Jobs

The AI Marketing Strategist will manage client relationships, advise on digital marketing, execute projects, and implement technology solutions to enhance marketing value.

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.

Lead the strategy, growth, commercialization, and transformation of Rockwell Automation’s FactoryTalk MES portfolio. Responsibilities include product and platform roadmap ownership, recurring revenue and profitability expansion, pricing and go-to-market strategy, customer and industry engagement, modernization programs, lifecycle management, and development of AI-ready, secure manufacturing capabilities. The role leads a product management team and collaborates across engineering, sales, customer success, security, and industry management.

Build and operate production AI agents for enterprise transformation. Own agent harnesses, tools, context, retrieval, classical ML components, evaluations, monitoring, tracing, guardrails, approval gates, and rollback paths. Develop reliable systems that work across complex enterprise landscapes, diagnose production failures, and generalize capabilities across customers. The role requires strong Python and TypeScript, experience with LLM-powered and agentic systems, retrieval, evaluation, and traditional machine learning.

Partners with AI, Product, Engineering, and business teams on financial planning, forecasting, budgeting, reporting, and strategic investment analysis. Owns AI cost reporting, tracks spend and anomalies, supports monthly close, performs budget-versus-actual and variance analyses, and improves financial data workflows and reporting infrastructure. Provides executive-level insights into financial performance, risks, opportunities, and product cost drivers.

Leads enterprise AI transformation initiatives, initially focused on Accounting and Finance. Assesses processes, builds and manages an AI roadmap, develops business cases, oversees cross-functional implementations, manages vendors and budgets, drives adoption, and measures productivity, cost, accuracy, and ROI improvements.

Work directly with the CEO on high-impact problems across operations, growth, finance, sourcing, and business intelligence. Own a department or product category, improve fulfillment and internal workflows, build AI-powered systems, develop supplier pipelines, support growth initiatives, create financial models and dashboards, and execute special projects. The role requires high agency, analytical strength, technical fluency, and comfort operating in a demanding onsite startup environment.

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.

Lead AI/ML solution architecture and proposal technical strategy for intelligence and homeland security customers. Design end-to-end AI/ML systems, author technical proposal volumes and graphics, engage customers, develop costing and staffing plans, mentor junior architects, and ensure compliant, compelling responses to RFIs/RFPs throughout capture and proposal lifecycles.

Integrate and deploy LLM-powered and other AI systems with databases, data lakes, ingestion pipelines, dashboards, and real-time processors. Develop system architecture, automate CI/CD using DevOps tools (Jenkins, Docker, Kubernetes), script integrations in Python, optimize LLM workflows for performance, and collaborate with national security analysts to meet operational needs.

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.

Lead design and implementation of AI/ML capabilities for classified, air-gapped and government-cloud environments. Architect and deploy on-prem LLMs, build RAG and embedding pipelines, enforce AI governance and security controls, support RMF/accreditation, establish CI/CD and containerized deployments, mentor developers, and brief government stakeholders.

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.

Design, develop, and deploy agentic AI solutions using existing large language models. Build multi-agent workflows, engineer prompts, integrate AI capabilities through APIs and microservices, and evaluate output reliability. Collaborate with system engineers, software developers, and mission experts to automate intelligence reporting and dissemination workflows. Apply AWS, Python, orchestration frameworks, RAG, and MLOps practices while maintaining production-quality AI capabilities.

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.