Top Artificial Intelligence Jobs

Lead the architecture, implementation, governance, and operation of enterprise AI agents using Amazon Bedrock AgentCore. Design agents, knowledge bases, action groups, orchestration workflows, registries, guardrails, and emergency shutdown controls. Guide ECS deployments, API Gateway integrations, IAM governance, authentication, and platform standardization. Provide technical leadership to engineering and DevOps teams while collaborating with AWS specialists, customers, and business stakeholders.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Owns the secure platform foundation for AI systems across cloud, hybrid, on-premises, and air-gapped environments. Responsibilities include platform architecture, GitHub Enterprise administration, Terraform automation, identity and secrets integration, software supply-chain security, backup and disaster recovery, observability, vulnerability remediation, threat detection, and incident response. The role also leads Zero Trust segmentation and operates reliable infrastructure supporting critical engineering platforms.

Sell Elsevier's ClinicalKey to hospitals, health systems, and medical schools by prospecting within an assigned territory, building C-suite relationships, creating account plans, delivering product demonstrations, closing new business to exceed quota, forecasting sales, maintaining CRM data, and collaborating with internal teams. Requires frequent customer engagement and travel.

Freelance operations consultant who maps and redesigns workflows, manages project backlogs, and builds AI-enabled internal tools. The role involves developing full-stack applications with Supabase and Vercel, replacing spreadsheets and no-code tools with secure scalable systems, and documenting processes for independent team adoption.

Build and maintain backend services, APIs, and integration layers for a modular ecommerce platform. Integrate personalization, semantic and LLM search, DAM, PIM, SAP/HANA, ERP, analytics, and customer data systems. Use AI coding agents throughout development while validating code quality, security, performance, and reliability. Design scalable APIs, automated testing, observability, and reusable architecture while collaborating with internal teams, vendors, and implementation partners.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Partner with executives to understand business operations, identify and prioritize high-impact AI opportunities, define ROI, and create actionable roadmaps. Collaborate with engineers to scope solutions, inform architecture, maintain alignment with business outcomes, present recommendations to C-suite stakeholders, and manage handoffs to implementation.

Build and improve an AI intelligence platform that transforms structured and unstructured data into business intelligence. Develop scalable data pipelines, dashboards, reporting systems, analytical models, AI workflows, prompt strategies, evaluation frameworks, and automation. Analyze restaurant and social media data to identify trends and opportunities, while monitoring quality, troubleshooting issues, and improving reliability and scalability. Establish best practices for AI-assisted analysis and mentor team members.

Sell Elsevier's ClinicalKey clinical search engine to hospitals, health systems, medical schools, and home health providers. Prospect, build account plans, demo remotely, manage CRM data, forecast pipeline, collaborate with internal teams, and meet/exceed quota with up to 40% travel.

Conduct interviews with partners, solution architects, and sales teams to identify enterprise AI use cases. Analyze findings, map customer needs to Red Hat’s AI platform and partner ecosystem, identify go-to-market opportunities, and present strategic recommendations to AI Ecosystem leadership.

Leads the design, development, optimization, and deployment of AI models and software applications. Develops APIs using Python and C#, manages complex AI projects, optimizes database queries, ensures data integrity, and collaborates with cross-functional teams. Responsibilities also include technical documentation, code reviews, bug fixes, mentoring junior engineers, and delivering AI solutions that meet business and performance requirements.

Leads a globally distributed engineering team building production-grade Generative AI products. Responsibilities include GenAI architecture, software development, LLM and RAG solutions, fine-tuning, evaluation, monitoring, security, cloud deployment, technical strategy, stakeholder communication, mentoring, and performance management.

Leads enterprise architecture for data and AI modernization across business domains. Drives metadata standardization, semantic data modeling, governance, analytics and AI enablement. Partners with executive stakeholders, product, engineering, data and AI teams; contributes hands-on to architecture, MLOps, AI model feedback and AI-generated code reviews.

Supports spacecraft and liquid rocket propulsion assembly, integration, and testing. Responsibilities include designing test systems, inspecting and troubleshooting hardware, conducting test operations, creating documentation, developing tooling and fixturing, improving production processes, and resolving technical issues. The intern collaborates across engineering teams and performs hands-on work with mechanical, fluid, electrical, pneumatic, hydraulic, and electromechanical systems.

Designs and integrates AI-enabled modernization solutions while establishing quality assurance frameworks for AI-generated code, skills, agents, and prototypes. Develops test plans, acceptance criteria, regression suites, automated testing, security scans, and quality gates. Collaborates with DevOps teams on reliable multi-agent workflows, validates prototypes for production readiness, and monitors accuracy, security, compliance, and data-sensitivity risks.

Develop and maintain production-scale AI/ML systems and infrastructure supporting Navy and DoD cyber defense, situational awareness, mission assurance, and command-and-control capabilities. Responsibilities include software design, coding, testing, debugging, deployment, ML model monitoring, data engineering, application lifecycle support, security architecture reviews, documentation, and agile team participation.

Leads enterprise software architecture, technical strategy, and roadmap execution across multiple domains. Designs scalable, reliable systems and platforms using modern programming languages, cloud services, microservices, databases, containers, and DevOps practices. Partners with product and engineering teams, mentors technical staff, influences executives, improves engineering processes, evaluates performance and costs, and drives continuous learning. The role requires extensive software development and architecture experience, strong cloud and Azure expertise, and the ability to transfer research into production.

Maintains and optimizes Oracle, SQL Server, and Sybase databases across large-scale compute and AWS environments. Responsibilities include database security, availability, disaster recovery, performance tuning, index maintenance, monitoring, user administration, data modeling, lifecycle management, database architecture coordination, and installation of database systems, tables, and views. Requires an active TS/SCI clearance with polygraph, 10 years of DBA experience, and a technical bachelor's degree or equivalent experience.

Designs, develops, tests, deploys, and improves machine learning models and algorithms for business and national security mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and monitoring models, interpreting performance metrics, conducting testing and evaluation, and supporting scalable AI/ML solutions. The role may coordinate distributed teams and requires strong communication, analytical, and problem-solving skills.

Manage, monitor, troubleshoot, and maintain large-scale Hadoop clusters and AWS cloud infrastructure supporting cyber intelligence and AI/ML capabilities. Configure Linux systems, networking, provisioning, hardware, security tools, and configuration management platforms. Develop Bash, Perl, or Python scripts; diagnose distributed storage systems; support Kubernetes, Docker, load balancing, and observability tools. Collaborate across mission teams, lead complex technical tasks, and mentor junior resources.

Design, develop, test, deploy, and improve AI/ML algorithms and models for complex mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and retraining models, monitoring performance, conducting testing and evaluation, and overseeing system integration. The role requires knowledge of application development, infrastructure, data engineering, governance, ML libraries, and performance metrics, with potential leadership of distributed teams.

Develop and sustain cloud software and distributed systems supporting cyber intelligence and AI/ML capabilities. Responsibilities include requirements analysis, Java software development, cloud deployment, integration, testing, RESTful services, analytics, Hadoop ecosystems, Linux systems, and technical documentation. The role requires AWS expertise, Kubernetes, infrastructure as code, CI/CD, microservices, large-scale data processing, and active TS/SCI clearance with polygraph.

Administers Oracle, SQL Server, and Sybase databases supporting cloud-based AI/ML and national security systems. Responsibilities include database security, integrity and availability; user account management; disaster recovery; performance analysis and tuning; index maintenance; monitoring; data modeling; policy development; DBMS evaluation; server administration; and database lifecycle activities. The role requires an active TS/SCI clearance with polygraph and five years of comparable DBA experience.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable production solutions. Requires strong expertise in machine learning algorithms, software architecture, data engineering, model validation, and system integration. The role may lead distributed teams and requires an active TS/SCI clearance with polygraph.