Top Artificial Intelligence Jobs

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.

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.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Build and improve Swayable’s AI and analytics engine using Python, scientific computing, machine learning, and distributed systems. Develop scalable, high-performance software, optimize parallel computing architecture, productize AI and data analysis capabilities, and collaborate with engineers, data scientists, and researchers. The role also includes designing new features, maintaining testable code, mentoring junior engineers, documenting decisions, and providing technical feedback.

Build and optimize Swayable’s AI and analytics engine using Python, scientific computing, machine learning, and distributed systems. Responsibilities include architecting scalable high-performance computing solutions, productizing AI and data analysis, developing new features, maintaining code quality, collaborating with engineers and researchers, and mentoring junior engineers.

Build and deploy production-grade AI applications using Python and Azure. Develop LLM, RAG, and agentic workflows; integrate enterprise platforms, APIs, databases, and Microsoft 365; and create scalable backend services. Partner with stakeholders to translate ambiguous business needs into production solutions while applying strong testing, security, observability, scalability, and performance practices.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.

Own budgeting, forecasting, financial modeling, monthly reporting, SaaS metrics, scenario analysis, and board reporting. Build and maintain AI-driven finance workflows using Claude, automate recurring analysis, validate outputs, and document data lineage. Partner with accounting, Data and Analytics, Sales, Marketing, Product, and G&A to explain results, manage budgets, evaluate investments, and deliver reliable reporting. Support month-end close and maintain trusted BI data sources.

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

Build and maintain production AI/ML applications, backend services, data pipelines, and machine learning models. Develop agentic AI systems, LLM and RAG solutions, and MLOps capabilities including monitoring, versioning, A/B testing, and retraining. Collaborate with product, software, platform, and data teams to deliver reliable, explainable, and performant AI solutions for financial markets.

Lead assessment and modernization of NASIC data management: inventory assets, design scalable secure architectures, implement lifecycle governance, ensure data quality and integration, and apply ML/AI and visualization to derive actionable intelligence. Provide recommendations, training, and capacity building for IC personnel.

Lead GEICO’s IVR and conversational AI product strategy, roadmap, and lifecycle. Drive voice experience design, self-service adoption, containment, analytics, and customer satisfaction through cross-functional collaboration with engineering, design, operations, and business teams. Responsibilities include market research, feature prioritization, platform architecture influence, IVR performance optimization, analytics implementation, and integration of NLP, voice biometrics, and AI-powered intent recognition.

Leads the design, development, deployment, and technical direction of production-grade AI systems, including LLM applications, retrieval pipelines, and model-driven services. Establishes practices for evaluation, testing, monitoring, reliability, and operationalization. Partners with product, data, engineering, and business stakeholders to develop scalable solutions while ensuring security, privacy, responsible AI, and enterprise compliance. Mentors engineers, guides architecture decisions, and mitigates technical risks across complex initiatives.

Lead the architecture, development, deployment, and governance of production-grade agentic AI systems supporting investment research, quantitative analysis, portfolio management, and trading. Mentor engineering teams, establish AI engineering standards, build agent-driven workflows, manage technical debt, and ensure responsible scaling across cloud-native platforms. Partner with investment and business stakeholders to translate requirements into scalable solutions that deliver measurable value.

Lead the AI Tooling and Ecosystem Team in building tools and services for datasets, prompts, model evaluations, human and LLM feedback, and AI workflows. Manage engineers, hire and develop talent, own multi-quarter roadmaps, and align AI, product, data science, and platform teams. Improve AI feature velocity, reliability, measurability, and cost efficiency through internal tools, APIs, evaluation systems, and human-in-the-loop pipelines.

Design and deploy AI-driven, closed-loop laboratory automation systems for biological and chemical discovery. Integrate laboratory hardware, robotics, firmware, APIs, sensors, and cloud AI infrastructure. Build data pipelines for high-throughput genomic, proteomic, and imaging data used in machine learning. Conduct wet-lab experiments, prototype technical features, translate experimental constraints across biology, chemistry, hardware, and machine learning teams, and communicate complex results clearly.

Design, build, and optimize LLM- and RAG-powered applications for government and enterprise missions. Develop document ingestion, embeddings, vector retrieval, LLM orchestration, traditional machine learning models, APIs, and cloud-native containerized workloads. Apply software engineering, machine learning, MLOps, security, and model evaluation practices to deliver reliable production systems. Collaborate with data engineers, architects, and mission subject-matter experts to create scalable solutions.

Leads Wayve’s data strategy for autonomous-driving AI, including data acquisition, discovery, curation, enrichment, training, evaluation, deployment, and performance measurement. Builds the data flywheel, identifies rare and safety-critical scenarios, connects data investment to model outcomes, scales global data architecture, and leads a multidisciplinary Data organization. Partners with AI, Science, Validation, Product, Compute, Finance, and Commercial teams.

Lead AI product management for FanDuel’s agentic products, owning strategy, roadmaps, prioritization, prototypes, evaluation systems, and outcomes. Manage and mentor AI product managers while defining AI-native product practices across the organization. Partner with engineering, platform, business, trust and safety, responsible AI, and legal teams to deliver production AI experiences with measurable quality, responsible-AI safeguards, and regulatory alignment.

Build and deploy production-grade AI, ML, and intelligent automation solutions for Finance use cases. Design scalable systems, data pipelines, automation workflows, web apps, and intelligent agents; collaborate with Finance, Product, Engineering, and IT to deliver, monitor, and scale solutions that improve efficiency and controls.

Build and maintain internal AI-powered applications using Retool, Python APIs, and enterprise LLMs. Integrate Snowflake, dbt, source-system APIs, and operational data while implementing HIPAA-compliant PHI controls, role-based access, masking, audit logging, and data minimization. Develop and evaluate prompts, design workflow-focused user experiences, measure adoption, gather feedback, and continuously improve tools supporting RCM, billing, authorization, and clinical operations.

Design, build, and operationalize production AI/ML systems on Google Cloud for federal defense programs. Lead Vertex AI, MLOps, data engineering, GenAI, security, and compliance initiatives; develop scalable pipelines and deployment platforms; implement monitoring, retraining, evaluation, and secure-by-design controls. Partner with mission stakeholders, mentor engineering teams, lead technical reviews, and document architectures, controls, and operating procedures.

Build and scale infrastructure automation and operations solutions that detect, prioritize, and remediate vulnerabilities. Develop AI/ML-driven analysis and anomaly detection, integrate security tooling (e.g., Tenable, CrowdStrike), and deliver CI/CD-enabled automation frameworks across large-scale cloud environments (AWS/GCP/Azure). Collaborate with engineers, security, and operations to harden systems and operationalize vulnerability management at scale.