Top Artificial Intelligence Jobs

Leads development of full-stack, cloud-native software using Java, Python, AWS, microservices, APIs, and PostgreSQL. Designs scalable and secure systems, builds LLM and generative AI capabilities, and integrates AI agents into engineering workflows. Improves operational stability through automation and observability while contributing to Agile delivery, system architecture, and technical standards. Collaborates across initiatives and promotes responsible, secure validation of AI-assisted development outputs.

Design and maintain formal ontologies, knowledge graphs, semantic reasoning engines, and data integration pipelines for secure defense and intelligence environments. Develop SPARQL queries, semantic APIs, ontology alignment strategies, governance frameworks, and validation processes. Collaborate with AI/ML engineers and mission stakeholders, support secure production deployments, document semantic architectures, and mentor junior engineers. The role requires expertise in OWL, RDF, RDFS, SPARQL, graph databases, semantic modeling, data integration, and secure data engineering.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, autonomy algorithms, and end-to-end pipelines. Translates business and mission requirements into solution architectures, builds and tests models, integrates APIs, deploys through CI/CD, and monitors performance. Collaborates with stakeholders, documents designs, supports user adoption, troubleshoots issues, and drives continuous improvement. The role also involves deep learning, generative AI, MLOps, knowledge graphs, cloud platforms, and research into emerging technologies.

Develops and validates predictive, machine-learning, and deep-learning models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset management, and eligibility for a Secret clearance as a U.S. citizen.

Leads the Applied AI Mission Integration team and STAR.Data product, overseeing product development, deployment, mission-system integration, and trusted AI solutions across Lockheed Martin business areas and external customers. The role manages high-performing technical teams, coordinates technology transitions, cultivates talent, and advances data-sharing capabilities for contested and bandwidth-constrained environments.

Develops, integrates, tests, and deploys AI/ML and autonomy algorithms for mission systems. Designs and performs AI red-team assessments covering adversarial attacks, prompt injection, data leakage, governance, compliance, and supply-chain risks. Builds automated testing tools, develops evaluation methodologies, collaborates with AI, platform, data, and cybersecurity teams, and advances secure, robust, trustworthy AI practices aligned with NIST AI RMF and MITRE ATLAS.

Improves the reliability, scalability, and observability of AI development and deployment platforms. Develops automation, CI/CD and GitOps workflows, dashboards, alerts, tests, and release validation processes. Investigates incidents, reduces operational toil, supports Kubernetes-based infrastructure, and collaborates across engineering teams on platform health. The role also includes autonomous-system integration, verification, validation, testing, and hardware/software interface troubleshooting.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, and end-to-end pipelines. Translates business needs into solution architectures, builds and fine-tunes models, performs integration testing, deploys through CI/CD, and monitors production systems. Collaborates with stakeholders, documents designs, supports user adoption, troubleshoots issues, and drives continuous improvement. The role also involves autonomy algorithms, perception, mission planning, controls, and human-machine interfaces.

Leads AI and machine learning engineering teams developing, integrating, testing, and maturing mission systems across the full software life cycle. Provides technical and functional leadership, supports flight testing and classified integration, advances AI and software engineering practices, and contributes to product roadmaps, recruiting, training, and new business capture activities.

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.

Architects autonomous components for Lockheed Martin products and platforms, defining hardware, software, and systems architectures from concept through deployment. Translates customer requirements into technical specifications, establishes design standards, evaluates components and solutions, and guides architecture across project teams. The role also supports machine learning model automation, cloud platforms, CI/CD, image-processing algorithms, and human-machine or machine-machine teaming.

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, integrates, tests, and deploys AI and machine learning algorithms for data engineering platforms. Builds intelligent orchestration, data quality validation, metadata discovery, anomaly detection, predictive operations, and automation capabilities. Integrates ML into ETL/ELT workflows and cloud data infrastructure, troubleshoots production systems, and creates interfaces enabling data teams to use AI tools. Requires expert Python, production ML, MLOps, cloud, data engineering, and leadership expertise.

Leads exploratory data analysis and applied AI research to identify patterns, validate hypotheses, and create business-focused insights. Develops, trains, validates, and deploys predictive and deep-learning models using statistical, optimization, GPU, distributed, and cloud computing techniques. Translates model results into actionable recommendations and visualizations, integrates analytics into business decisions, maintains CI/CD pipelines, and collaborates with cross-functional teams on scalable AI architectures.

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.

Develops production-grade AI and machine-learning solutions, including LLM integrations, data pipelines, autonomy algorithms, testing, deployment, monitoring, and continuous improvement. Translates business and mission requirements into AI architectures, integrates models into applications, and collaborates with stakeholders to deliver measurable value. Requires Python, deep-learning, generative AI, REST API, and software-development expertise, with preferred experience in MLOps, cloud platforms, knowledge graphs, containers, and Git.

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.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, and end-to-end pipelines. Translates business requirements into architectures, builds and fine-tunes models, integrates REST APIs, conducts testing, deploys through CI/CD, monitors solutions, documents designs, and drives continuous improvement. The role also supports autonomy software development, human-machine interfaces, algorithm integration, troubleshooting, and mission-system testing.

Leads customer-facing deployment of an internal AI/ML Ops platform, owning solution discovery, architecture, implementation, deployment, and adoption for MLOps and GenAI workflows. Identifies platform weaknesses and drives remediation with core engineering teams. Provides technical leadership, develops production AI/ML systems, and works across Kubernetes, GPU-backed, cloud, and on-premises environments while collaborating with customers and stakeholders.

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.

Develop and deploy machine learning and autonomy algorithms for radar tracking systems. Research advanced ML applications, build and integrate tracking capabilities, assess model performance using customer data, and support design, testing, troubleshooting, and fielding. Collaborate with cross-functional teams to address operational requirements while contributing across the software development lifecycle. The role is on-site in Moorestown, New Jersey and requires U.S. citizenship.

Develops and deploys production-grade agentic AI systems, including tool-calling, RAG pipelines, and autonomous workflows. Builds platform features, test automation, security enhancements, and GitLab CI/CD pipelines for containerized services. Implements vulnerability remediation, authentication, compliance artifacts, observability, and AI evaluation practices. Requires strong Python engineering, production GenAI experience, cloud and DevSecOps skills, and collaboration across the platform. US citizenship is required for program access.