Top Artificial Intelligence Jobs

Build and operate production private AI platform capabilities (agentic/multi-agent systems, orchestration, auto fine-tuning, runtime/model optimization). Implement evaluation, monitoring, safety/rollback controls, developer-facing APIs, and tooling. Partner with data and infra teams to ensure reliable, performant AI inference in constrained edge environments.

Designs, develops, tests, and supports Java-based cloud-native applications on Google Cloud Platform. Builds REST APIs, integrates AI services, applies secure coding practices, contributes to CI/CD and deployment automation, and implements observability through logging, metrics, dashboards, and alerts. Troubleshoots production issues, performs root-cause analysis, participates in architecture discussions and code reviews, collaborates across engineering and business teams, and mentors junior developers.

Design, prototype, deploy, and support generative and predictive AI solutions for U.S. Government customers. Develop interfaces and applications, agentic workflows, and LLM fine-tuning capabilities; research emerging AI techniques; translate technical concepts for customers and subject matter experts; and support pre-sales activities. The role requires Python development, AI/ML modeling, RAG technologies, LLM frameworks, APIs, and government-sector experience.

Participate in a remote, 10-minute audio interview moderated by AI. Respond verbally to automated prompts and share honest opinions about conversational AI and digital experiences. Participants need clear conversational communication, a reliable microphone, and a quiet environment. No technical background or preparation is required.

Develop and manage scientific publications, congress materials, medical content, FAQs, advisory board materials, and literature summaries for oncology products. Lead publication submissions and reviews while ensuring compliance with ICMJE, GPP, journal, regulatory, and ethical standards. Contribute strategically to responsible AI integration, including monitoring healthcare provider use of AI tools and improving content discoverability and integrity. Manage multiple projects, collaborate with internal and external stakeholders, and support lung cancer and neuro-oncology communications.

Lead the architecture, build, and deployment of AI-driven automation and intelligent systems to solve security, compliance, and risk challenges. Develop real-time intelligence, workflow automation, and risk-based decision-making tools, and engage customers and partners to refine and deploy production-grade solutions.

Lead integration of LLMs into ASIC physical design workflows: identify automation targets, build agentic PnR loops, evaluate LLM outputs versus sign-off criteria, create RAG pipelines indexing PDKs and runbooks, write Tcl bridge scripts to translate suggestions into EDA tool commands, and drive prototype-to-production delivery to reduce design cycle time.

Own the strategy, requirements, evaluation, and execution of AI agent products and systems. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, and ensure AI features are understandable, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions and reliable real-world workflows.

Lead design and deployment of AI solutions (copilots, agents, RAG) into client workflows: analyze processes, coordinate pilots, integrate prompt libraries and APIs, support change management, track adoption, and iterate post-launch.

The AI Enablement Associate will support AI tool rollout, document workflows, track adoption metrics, and collaborate with stakeholders to enhance AI solutions for Wealth Management. Engagement and training of Financial Advisors are key responsibilities, alongside managing adoption initiatives and synthesizing user feedback.

Develop and support full-stack and AI-driven software across the enterprise. Responsibilities include designing, testing, deploying, and continuously improving applications; modernizing legacy IBM i systems; adopting cloud technologies; supporting integrations, batch jobs, file transfers, and data workflows; troubleshooting production issues; and contributing to AI, machine learning, LLM, RAG, and agent-based solutions. The role also involves cross-functional collaboration, release support, monitoring, incident management, and participation in a 24x7 on-call model.

Analyze how AI models are deployed in production across hyperscalers and enterprises, evaluate inference-time tradeoffs (latency, throughput, cost), model economic impacts on product margins, diligence AI company infrastructure and unit economics, and translate deployment-level technical choices into investment-grade economic insights.

Design, build, and deploy agentic AI systems, orchestration pipelines, and RAG/knowledge retrieval services. Implement scalable backend services, observability, LLMOps practices, and integrations with code repositories and enterprise systems. Mentor engineers and ensure explainable, traceable AI for regulated federal healthcare environments.

Design, prototype, and productionize applied AI capabilities (LLMs, RAG, agents) to improve reasoning, explainability, and developer productivity. Optimize retrieval, embeddings, multi-agent workflows, and evaluation frameworks; build benchmarks and diagnostics. Collaborate with architects and engineers to ingest enterprise knowledge, refine semantic search, and transition experiments into FedRAMP-aligned production for federal healthcare modernization.

Design and build identity services for AI agents and enterprise systems: authentication/authorization (OAuth2/OIDC/JWT/SAML/mTLS), credential lifecycle, enterprise IdP integrations, secure agent access, developer-facing identity SDKs/APIs, auditability, and compliance alignment for FedRAMP/Zero Trust environments.

Lead end-to-end front-end development for an enterprise Agentic AI platform. Design interactive, explainable UIs that render streaming LLM outputs, code citations, dependency graphs and authoring workflows. Build real-time experiences with WebSockets/streaming APIs, ensure traceability to source code, collaborate with AI engineers and designers, and drive accessibility, performance, and observability across a FedRAMP-aligned federal deployment.

The Partnership Associate is responsible for building relationships with independent insurance agencies, conducting discovery calls, and supporting outreach strategies to enhance sales pipelines.

Lead strategic enterprise sales across the US for AI, LLM, and quantum infrastructure solutions. Manage full deal lifecycle for large, complex opportunities, engage C-suite stakeholders, co-create tailored AI use cases, and drive long-term account expansion through cross-functional collaboration and go-to-market execution.

Lead integration and sustainment of an AI-driven knowledge management platform. Define system architecture, oversee integration and deployment, manage CI/CD and Agile practices, and ensure secure, reliable, and scalable system delivery with verification, validation, and lifecycle sustainment.

Lead design and operationalization of AI/ML and LLM-enabled applications to automate workflows and support intelligence analysis. Architect scalable, cloud-native solutions, mentor engineers, and translate emerging AI tech into mission-ready capabilities.

Lead AI infrastructure projects, develop GenAI solutions, drive automation strategy, and collaborate with IT and business teams for implementation.

Design and build AI- and automation-driven solutions to accelerate Mortgage Cadence Platform configuration using LLMs and workflow automation. Develop integrations with system APIs, partner with implementation teams, create documentation and training, and propose new tools to improve implementation accuracy and scale.

Partner with professional-services firms to discover and automate high-value workflows using Intapp Celeste. Deploy and iterate agents, playbooks, and semantic/ontology mappings; drive adoption through scoping, onboarding, training, and expansion; advise product and industry teams with real-world practitioner expertise. Act as a client-facing advisor and implementer, shaping product roadmaps while enabling Client Success to scale usage across a firm.

Lead product strategy and delivery for an agentic AI B2B platform. Write requirements, manage engineering ceremonies, drive client discovery and adoption, define metrics, partner with GTM, and extend AI capabilities across professional services firms.

Partner with client firms to discover and automate high-value workflows using Intapp Celeste. Map workflows to agents, playbooks, and semantic layers; codify firm methods; drive adoption through training and onboarding; enable scalable expansion and inform product strategy with real-world experience.