Top AI Engineer Jobs

Leads end-to-end delivery of AI solutions in production, translating customer needs into roadmaps and managing schedules, budgets, resources, and risks. Serves as a technical liaison across multidisciplinary teams, overseeing prototyping through deployment and support. Builds data pipelines, analytics capabilities, dashboards, and KPI tracking to measure efficiency gains. Requires Python, machine learning, project and budget management, stakeholder negotiation, and U.S. citizenship with eligibility for a Secret clearance.

Researches and develops AI, machine learning, computer vision, robotics, signal processing, and control algorithms for autonomous defense and commercial systems. Builds and tests prototypes, trains and optimizes models, defines algorithm requirements, and collaborates across perception, planning, simulation, and testing teams. The role also includes developing MLOps demonstrations, proof-of-concept environments, technical presentations, customer discovery support, RFP responses, and sales engineering enablement.

Develops and validates predictive and deep-learning models through exploratory research and statistical analysis. Designs scalable GPU, distributed, and cloud computing solutions for large datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role uses machine learning, optimization, simulation, and potentially NLP or Generative AI to deliver business value.

Develops and validates predictive, deep-learning, and optimization models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes.

Build and operate an enterprise AI/ML platform, developing automation, infrastructure-as-code, CI/CD and GitOps workflows, Kubernetes environments, observability, security controls, and self-service tooling. Integrate and test autonomous hardware and software systems, translate requirements into specifications, coordinate subsystem testing, analyze results, and resolve interface and system issues. Collaborate with engineering, cybersecurity, IT, data science, vendors, and platform users while reviewing designs and mentoring engineers.

Leads AI and machine learning research and engineering teams developing autonomous systems and mission applications. Oversees algorithm research, model development, prototyping, testing, deployment, and optimization across reinforcement learning, deep learning, computer vision, signal processing, and control systems. Manages workloads, resources, project delivery, technical standards, stakeholder communication, and employee development while identifying opportunities to apply emerging AI technologies.

Leads research and development of AI, machine learning, robotics, and autonomy technologies for defense and commercial applications. Directs technical teams, advances algorithms for perception, planning, control, and mission systems, and guides technologies from research through prototype development, integration, evaluation, and delivery. Builds customer relationships, shapes technical strategies and R&D programs, manages research portfolios, and communicates complex AI concepts to executives, scientists, customers, and program leaders.

Develops and validates predictive and deep-learning models through exploratory research, statistical analysis, and large-scale data processing. Designs GPU, distributed, and cloud computing solutions; identifies patterns in complex datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset experience, U.S. citizenship, and eligibility to obtain a Secret clearance.

Researches and develops AI and machine learning algorithms for autonomous missile and fire control systems. Builds, trains, validates, optimizes, and deploys deep learning models for object detection, classification, computer vision, and mission systems on CPUs, GPUs, and edge devices. Develops simulations and prototypes, analyzes system performance, supports hardware-in-the-loop and flight testing, and collaborates across perception, planning, modeling, and testing teams.

Lead reliability improvements for AI development and deployment platforms through software engineering, automation, observability, testing, and incident-response enhancements. Own reliability initiatives from design through implementation and adoption, standardize CI/CD, GitOps, progressive delivery, and telemetry practices, and develop reusable tooling for health validation and recovery. Integrate and test autonomous hardware and software systems, coordinate verification and validation, analyze test results, mentor engineers, and collaborate across teams.

Serves as the senior hardware expert for autonomous systems, designing and integrating sensors, compute, storage, controls, wiring, and embedded components. Supports hardware-software integration, system safety, certification, deployment, testing, and field operations. Develops hardware solutions for autonomous ground vehicles, performs electrical debugging, creates test procedures and verification software, and ensures designs meet architecture and program requirements. The role also provides technical leadership for FPGA, wiring harness, autonomy platform, and long-duration field-test activities.

Develop and deliver AI/ML-enabled enterprise, cloud, and application software for national security space programs. Support software design, development, integration, testing, mission management, command and control, and data analytics. Collaborate with engineering teams on mission-focused capabilities using object-oriented programming, AI prompting, model training, evaluation, data preparation, Agile practices, unit testing, and configuration management.

Develop and deploy scalable Generative AI and machine learning solutions for engineering workflows. Responsibilities include identifying AI opportunities, building prototypes and production applications, designing LLM retrieval and agentic workflows, integrating APIs and data, implementing testing and monitoring, and applying cybersecurity, DevSecOps, architecture, and responsible AI governance. The role partners with engineering, architecture, data, and IT teams to mature secure enterprise AI capabilities.

Researches and develops AI, machine learning, computer vision, signal processing, and control algorithms for autonomous defense and commercial systems. Builds, trains, evaluates, and optimizes models; develops prototypes and data products; defines requirements; and collaborates across perception, mission planning, simulation, testing, and customer application teams.

Develop and deliver AI/ML, enterprise, cloud, and application-based software for national security space programs. Support software design, development, integration, testing, data analytics, mission management, and command-and-control capabilities. Collaborate with engineering teams, apply coding standards, perform unit testing, and use configuration management and Agile practices. The role requires familiarity with object-oriented programming, AI/ML concepts, Linux or UNIX, and software development lifecycle activities.

Develop and deploy scalable full-stack web applications with AI-powered features. Build responsive frontends, backend services, REST APIs, databases, authentication, chatbots, automation tools, and intelligent workflows. Integrate LLMs, AI APIs, machine learning models, RAG, embeddings, and AI agents. Optimize performance, security, reliability, and scalability while testing and deploying through CI/CD pipelines. Collaborate with product, design, and engineering teams in a fully remote environment.

Design and implement AI-first business processes for both sides of Coquina’s public-sector marketplace. As a founding engineer, the role will help build the company from the ground up and later hire and manage an engineering team. The position requires hands-on AI-native development, high autonomy, rapid execution, and strong commitment to building startup culture. This is an onsite role five days per week at the company’s Herald Square office in New York.

Designs, builds, deploys, and validates AI agents, LLM applications, expert systems, and intelligent automation solutions. Responsibilities include researching open-ended AI problems, translating business workflows into technical specifications, developing decision-making systems, documenting automated processes, and supporting enterprise-scale, multi-region deployments. The role also requires stakeholder collaboration, end-user adoption, and expertise with Microsoft Copilot Studio, Dataiku, Azure, DevOps, and GitHub.

Build and maintain end-to-end applications using React, Golang, and Node.js. Leverage AI development tools to improve speed and quality, collaborate with product and design, contribute to deployment and DevOps, and own features from concept through release. The role requires proactive problem-solving, rapid learning, scalable system design, and a strong builder’s mindset in a fast-paced energy and logistics technology environment.

Lead the architecture, strategy, and development of enterprise generative AI initiatives. Define AI/ML standards, guide scalable model deployment, conduct advanced research, and collaborate with executives, product, data science, and engineering teams. Mentor technical leaders, promote MLOps and responsible AI practices, optimize models for cost and performance, and represent the organization through publications and industry engagement.

Build and scale production-grade agentic AI systems using Google ADK and related platforms. Lead technical strategy, architecture, integrations, testing, deployment, monitoring, and lifecycle management for multi-agent workflows. Integrate agents with enterprise systems through APIs and MCP, establish observability and quality practices, lead complex cross-functional projects, and mentor engineers on agent development, safety, and operational excellence.

Design, build, deploy, and maintain production-grade agentic AI systems using Google ADK and related platforms. Lead technical strategy and complex agent projects, architect multi-agent workflows, integrate enterprise tools and APIs, and ensure service reliability through testing, observability, telemetry, and lifecycle management. Mentor engineers, influence technical decisions, and drive innovation in agent development, orchestration, safety, and performance optimization.

Define enterprise-wide agentic AI strategy and architecture using Google ADK and related platforms. Build multi-agent systems, governance frameworks, integrations, safety guardrails, monitoring, and operational standards. Influence technical direction across organizations, mentor senior engineering talent, lead complex initiatives, evaluate emerging AI technologies, and represent UKG through partnerships, publications, and industry engagement.

Build, deploy, and maintain production-grade agentic AI systems using Google ADK and related platforms. Develop agent workflows, tools, integrations, orchestration patterns, testing frameworks, observability, and lifecycle operations. Collaborate with product and engineering teams, contribute to architecture and design decisions, review code, troubleshoot service health, document solutions, and mentor engineers. Apply DevOps practices and evaluate emerging agentic AI technologies to improve enterprise workforce platform capabilities.

Build and operate an enterprise AI governance platform, including AI registration, risk tiering, evaluation, monitoring, observability, evidence capture, incident response, and rollback processes. Develop governance controls for machine learning, generative, retrieval-augmented, and agentic systems. Partner with technology, security, privacy, compliance, HR, and business teams to improve responsible AI adoption while reporting on AI quality, usage, cost, and value.