Top Artificial Intelligence Jobs

Lead complex data exploration and analytics for DoD/IC, develop and apply statistical and AI/ML models, evaluate AI methodologies, perform TEVV and AI risk mitigation, and guide teams working on classified networks to produce actionable intelligence.

The Engineer II will perform complex engineering tasks, lead projects in thermoforming engineering, manage project costs, and support continuous improvement initiatives while promoting safety and collaboration within the team.

Perform data science for defense intelligence: collect/clean data, build statistical and ML/NLP models, code in Python/SQL on AWS, produce dashboards/reports (Tableau), and support analysts. Work independently on moderate-complexity problems; active TS/SCI with polygraph required.

Design, optimize, and deploy machine learning models for mobile, embedded, and edge devices. Responsibilities include model compression, quantization, hardware-specific performance tuning, cross-platform inference runtimes, secure model execution, hybrid edge-cloud architectures, telemetry, benchmarking, and on-device updates. The engineer collaborates with hardware, firmware, and product teams while addressing privacy, security, bias, documentation, and emerging edge AI technologies.

Build and operate petabyte-scale data pipelines for AI training, evaluation, and continual improvement. Responsibilities include multimodal data ingestion, cleaning, deduplication, quality assurance, dataset versioning, lineage, high-throughput GPU data delivery, labeling workflows, privacy enforcement, observability, storage architecture, and cost optimization. The engineer will collaborate with ML researchers and engineers while documenting systems and staying current with AI data infrastructure technologies.

Leads enterprise architecture, AWS cloud transformation, application modernization, and Agentic AI platform initiatives. Designs hybrid cloud, microservices, distributed systems, multi-tenant SaaS, APIs, event-driven architectures, CI/CD, DevSecOps, and Infrastructure-as-Code solutions. Establishes architecture governance, security, compliance, and observability practices while collaborating with executives and engineering teams. Provides technical leadership, conducts design reviews and workshops, mentors delivery teams, and guides successful execution of transformation programs.

Establishes and operates enterprise Data and AI governance frameworks across the full lifecycle. Develops responsible AI, privacy, security, risk, compliance, and model assurance controls; oversees data and model governance, vendor requirements, monitoring, and audit readiness. Partners with legal, cybersecurity, compliance, architecture, risk, and business teams to embed governance into delivery processes. Creates standards, tooling, training, metrics, and reporting while monitoring evolving regulations and technology trends.

Build and lead a greenfield cloud platform supporting application, data, and AI workloads. Responsibilities include managing Terraform infrastructure, production EKS clusters, Helm deployments, CI/CD pipelines, security controls, observability, cloud cost allocation, and developer golden paths. The role establishes engineering standards, supports regulated-data compliance, partners across engineering teams, reviews infrastructure changes, mentors engineers, and writes production code while ensuring reliable, secure, and cost-effective service delivery.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Provide legal counsel for procurement and vendor contracting: draft, review, and negotiate contracts; advise on data privacy, information security, AI, IP, and regulatory compliance; perform legal research and due diligence; manage outside counsel; partner with Procurement, Cyber Security, Privacy, Third Party Risk Management, and Compliance; develop templates, playbooks, and contracting process improvements.

Leads enterprise architecture, technical strategy, and evolution of GEICO’s Generative AI, virtual agent, and agentic workflow platforms. Designs scalable systems for retrieval, knowledge management, prompt and context management, orchestration, semantic search, and action execution. Evaluates technologies, establishes GenAI application standards, guides production deployments, influences business strategy, mentors senior engineers, and serves as a final technical authority across multiple teams and business domains.

Architect and implement enterprise-scale AI systems for financial and alternative data, including multimodal ML pipelines, NLP, LLMs, computer vision, deep learning, and conversational AI. Lead MLOps, model deployment, observability, RAG, semantic search, and continuous learning initiatives. Collaborate with product and research teams, establish engineering standards, mentor engineers, and advance AI-driven financial analytics solutions.

Lead the AI research agenda for Private Assets R&D, developing explainable models for pricing, comparables, nowcasting, and cash-flow forecasting. Design agentic AI workflows, automate research tasks, and translate research into scalable client-facing products. Collaborate with research, product, technology, and data engineering teams while representing the group in client engagements and published research.

Leads advanced AI and machine learning development for financial products, including NLP, causal ML, deep learning, reinforcement learning, and generative AI. Builds production-grade models, reusable pipelines, AI evaluation and guardrails, and experimental MVPs. Owns technical roadmaps and architecture, ensures model accuracy, explainability, fairness, and compliance, and mentors engineers while communicating complex concepts to senior stakeholders.

Supports AI and machine learning modernization projects by identifying integration opportunities, developing and testing AI-assisted tools, researching frameworks and APIs, collaborating with scientists and IT teams, and documenting workflows and outcomes. The intern will help automate internal business and laboratory processes and must work onsite in the Pittsburgh metro area during Fall 2026.

Leads enterprise SEO and AI discovery strategy across search engines, answer engines, and AI platforms. Owns roadmaps, technical discoverability, content architecture, entity optimization, structured data, KPI reporting, experimentation, and competitive analysis. Partners with marketing, product, UX, engineering, and content teams to improve visibility, citations, recommendations, traffic, engagement, and conversions. Influences backlogs, communicates executive recommendations, and coaches SEO and content contributors.

Manage and govern the Microsoft 365 AI and automation platform (Power Platform, Copilot Studio, Azure AI). Own architecture, environment strategy, DLP and connector governance, integration with Azure and enterprise systems, monitoring, cost control, security compliance, and a Power Platform & AI Center of Excellence while partnering across infrastructure, security, and business teams.

Design, maintain, and scale secure AI and automation solutions for enterprise productivity. Build API- and connector-based workflows, configure centralized platforms, document standards, train users, and partner with technical and business stakeholders to reduce manual work and improve operational efficiency.

Build and operate an enterprise AI governance program: author the AI-SSDLC, gate agent deployments, lead threat modeling/red teams, monitor AI behavior and compliance, align with Legal/Privacy/Compliance, and implement automation and dashboards for executive reporting.

Assist cloud and generative AI teams in designing, building, testing, and deploying AI agents, agentic workflows, AI-powered web applications, RAG pipelines, and enterprise LLM integrations. The intern will support prompt engineering, evaluate AI outputs, write documented Python code, participate in reviews, gather requirements, and collaborate with cross-functional teams in a hybrid environment.

Build the core agentic platform for global trade, including tool routing, policy enforcement, memory, context graphs, evaluation infrastructure, APIs, and scalable production systems. The role focuses on making AI agents reliable enough to execute financial and operational actions using messy enterprise data. Responsibilities include product ownership, customer collaboration, system design, production delivery, and potentially recruiting and mentoring engineers.

Designs, develops, and delivers scalable, secure software solutions; resolves complex technical issues; optimizes performance and reliability; builds cloud-native systems; supports CI/CD, automation, containers, monitoring, and infrastructure provisioning. The role applies security and compliance best practices, manages technical integrations and evaluations, collaborates with stakeholders, reviews code, maintains documentation, mentors engineers, and drives continuous improvement.

Owns enterprise AI product strategy, roadmaps, backlogs, requirements, governance, and prioritization across multiple platforms. Leads stakeholder alignment, Agile ceremonies, UAT standards, release sign-off, testing strategy, automation adoption, and root-cause analysis. Partners with UX, architecture, QA, and release teams to ensure delivery readiness, monitors product performance, gathers feedback, and advises senior leadership on product decisions and measurable business outcomes.

Build and operate custom AI agents for private-market clients end to end, from discovery and requirements through deployment, rollout, adoption, and ongoing iteration. Design agentic systems with retrieval, context design, tool and MCP integrations, orchestration, evaluation, observability, and cost controls. Work directly with clients, make product and engineering trade-offs, manage cloud infrastructure, document solutions, train users, and mentor less experienced engineers.

Build and operate custom AI agents for private-market clients across discovery, requirements, design, full-stack development, deployment, and post-launch iteration. Translate ambiguous business needs into reliable software, integrate tools and client systems, establish evaluation and observability practices, and support adoption through documentation, training, and rollout. Collaborate directly with clients and internal platform teams while balancing speed, security, maintainability, and product impact.