Top Artificial Intelligence Jobs

Build and maintain API integrations, data pipelines, web applications, and AI-powered solutions using LLM APIs. Support databases, deployments, debugging, testing, authentication, and application security. Improve UI/UX, evaluate AI-generated outputs, investigate emerging technologies, and contribute to workflow improvements. The role is suited to current students, recent graduates, or candidates with equivalent practical experience and emphasizes hands-on AI development, collaboration, and continuous learning.

Build and deploy computer vision models for live and recorded video feeds. Responsibilities include curating and annotating datasets, training and tuning YOLO models, processing video with Python, developing inference pipelines, evaluating model performance, and delivering production-ready code in an Agile environment. The role supports a federal border security mission and requires onsite work in Ashburn, Virginia, U.S. citizenship, and successful completion of a CBP background investigation.

Leads the design, development, and production deployment of AI solutions for healthcare payer applications. Responsibilities include architecting AI models and integrations, developing scalable production systems, evaluating model accuracy and fairness, translating business workflows into AI-enabled automation, researching emerging techniques, advising stakeholders, and mentoring junior engineers.

Supports responsible enterprise AI adoption by documenting use cases, engaging users, analyzing usage and feedback, developing enablement materials, testing prompts and workflows, and communicating recommended practices. The intern creates dashboards and presentations, evaluates AI program impact, promotes safe AI use, and assists with strategic projects under mentorship.

Owns the implementation, hardening, tuning, and polish of game AI behaviors and systems for shipping quality. Collaborates with design, animation, and other disciplines to create believable enemy behavior and strong player experiences. The role requires expertise in Unreal Engine, modern game AI architecture, animation systems, and C++, along with experience shipping at least one game focused primarily on AI, preferably a AAA action or FPS title.

Own enterprise AI platform integrations, including identity, connectors, APIs, provisioning, and control-plane guardrails. Lead AI support, user onboarding, enablement, technical escalations, stakeholder relationships, and vendor partnerships. Establish support tiers, SLAs, runbooks, observability, governance evidence, and risk-review processes. Partner with application development teams on platform handoffs, manage entitlements and decommissioning, and guide adoption across high-touch business groups.

Leads enterprise agentic AI strategy, architecture, and delivery. Builds scalable AI platforms, foundational services, frameworks, evaluation pipelines, model gateways, and observability capabilities. Manages and mentors AI engineering teams, partners with architecture, security, data, DevOps, and product leaders, and establishes engineering practices for reliable, secure, cost-effective AI deployment.

Design, build, deploy, and optimize AI/ML and generative AI solutions on Databricks. Develop scalable Spark and Delta Lake data pipelines, operationalize models with MLflow, and build RAG and LLM applications. Implement MLOps practices including CI/CD, monitoring, governance, and model deployment. Collaborate with technical, data, engineering, and business teams to deliver enterprise-scale AI platforms and analytics solutions.

Develop and scale AI, Microsoft Copilot, Power Platform, and intelligent automation solutions across Buildings. Responsibilities include designing AI agents and workflows, identifying automation opportunities, delivering training and adoption programs, supporting governance, integrating Microsoft platforms, and measuring business value through analytics and reporting.

Technical Lead responsible for architecting, deploying, operating, and sustaining secure agentic AI, LLM inference, data, and platform capabilities across classified and unclassified environments. Responsibilities include JWICS hosting, cross-domain data flows, cloud architecture, observability, security hardening, integration and performance testing, troubleshooting, documentation, accreditation support, and technical leadership. The role is onsite at the Pentagon and requires routine domestic and international travel.

Analyze structured and unstructured data from multiple sources; identify patterns, risks, and actionable insights; support LLM, RAG, NLP, and Generative AI applications; develop prompts; evaluate AI outputs; prepare and validate data; create reports and visualizations; and apply data governance, security, privacy, and responsible AI practices.

Build and support production AI platform capabilities for PlayStation’s digital commerce ecosystem. Develop LLM-powered services, reusable components, scalable APIs, microservices, and event-driven workflows using Python or Java. Collaborate with stakeholders to identify and prototype AI use cases, measure outcomes, and productionize successful solutions. The role emphasizes reliable, secure, cost-efficient backend engineering and practical applied AI, including integrations with model APIs, business workflows, data, documents, and internal services.

Designs scalable technical solutions for Intelligence Community and Department of War contract opportunities. Partners with customers, business development, engineering, and leadership to assess mission requirements, produce system architectures and operational workflows, develop prototypes and demonstrations, and create technical proposal content. The role requires expertise in cloud and hybrid infrastructure, AI integration, networking, security, compute, and storage, along with strong communication and customer engagement skills.

Design, build, and deploy AI-driven products, agentic systems, and end-to-end workflows for commercial real estate users. Own features from user discovery through architecture, implementation, and deployment. Develop evaluation frameworks, optimize context management and retrieval, translate domain expertise into technical requirements, and continuously benchmark emerging LLM models and agentic frameworks.

Design and implement enterprise architecture and integration solutions using Microsoft Azure and other cloud platforms. Analyze client requirements, develop technical designs, lead project planning and delivery, manage client relationships, and identify growth opportunities. Mentor teams, manage performance, evaluate emerging technologies, resolve stakeholder conflicts, and ensure adherence to professional, technical, quality, and independence standards.

Leads finance and FP&A strategy for Microsoft’s engineering organization, advising senior leaders on resource allocation, investment optimization, operating leverage, business health, risks, and tradeoffs. Develops financial models, scenario analyses, analytical frameworks, and process improvements to align engineering resources and spending with strategic priorities. Collaborates across Engineering, Finance, Procurement, Human Resources, and other corporate teams while managing direct reports and influencing decisions in a complex, matrixed environment.

Designs, develops, tests, and optimizes AI and machine learning systems across business problems. Applies machine learning, deep learning, transformer architectures, statistical reasoning, and NLP to build models and pipelines. Uses cloud platforms and distributed computing, collaborates with MLOps engineers on deployment, and follows software engineering practices. Responsibilities span the data science lifecycle, including data preparation, feature extraction, model development, deployment, maintenance, and communication with nontechnical stakeholders.

Build scalable, cost-efficient data pipelines and processing systems for AI model training. Responsibilities include discovering audio data sources, creating reliable ingestion pipelines, optimizing data quality and throughput, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, Python and Bash proficiency, and independent problem-solving.

Build and operate large-scale data pipelines that source, process, and improve audio datasets for AI model training. The role focuses on data quality, throughput, cost optimization, production systems, and collaboration with AI scientists. Responsibilities include discovering audio sources, implementing scalable processing systems, owning engineering projects, and continuously improving training-data infrastructure.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through continuous improvement. Requires strong backend software engineering, Python and Bash proficiency, production systems experience, and independent problem-solving.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and proficiency in Python, Bash, and Linux, with preferred experience in web crawling, distributed processing, cloud infrastructure, Terraform, ML datasets, or audio processing.

Build and operate large-scale data pipelines that acquire, process, and improve audio datasets for AI model training. The role involves optimizing data quality, throughput, and cost; collaborating with AI scientists; exploring data sources; and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training, including discovering data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. The role requires backend software engineering, production systems experience, and independent ownership of projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that collect, process, and improve audio datasets for AI model training. Responsibilities include discovering data sources, optimizing data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering experience, Python and Bash proficiency, Linux expertise, and production systems experience.

Build and operate large-scale data pipelines that create high-quality audio datasets for AI model training. Responsibilities include discovering audio data sources, optimizing data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and independent problem-solving in an ambiguous environment.