Top Artificial Intelligence Jobs

Owns enterprise architecture strategy for agentic AI infrastructure, including orchestration, identity, gateways, policy services, model hosting, and runtime environments. Defines roadmaps, standards, reference architectures, operational requirements, and production-readiness criteria. Leads complex cross-domain architecture decisions, builds prototypes, evaluates emerging technologies, guides engineering adoption, resolves risk and cost tradeoffs, and mentors architects and infrastructure engineers.

Own Calendly’s user engagement strategy across in-product and email experiences. Build ranking, timing, and trigger systems that encourage users to return while protecting brand trust. Lead ML-powered personalization and experimentation informed by qualitative user insights, balancing company objectives with user needs. Ship quickly, learn from real usage, and drive sustainable long-term growth. This hybrid role requires working from the San Francisco office one to three times weekly and may involve occasional travel.

Designs, develops, deploys, and maintains secure AI-integrated enterprise applications using C#/.NET, Python, Azure, LLM APIs, and modern front-end frameworks. Responsibilities include building APIs and interfaces, integrating and monitoring AI capabilities, managing cloud infrastructure and databases, implementing authentication and governance controls, evaluating prototypes, documenting systems, and supporting production handoffs. The role collaborates with managers, technical teams, attorneys, and legal professionals to deliver scalable AI tools in a regulated enterprise environment.

Build and own full-stack AI applications, data APIs, dashboards, and cloud-native services. Develop Python and Node.js backends, React frontends, AI/ML integrations, RAG pipelines, authentication, API management, testing, CI/CD, and performance improvements. Collaborate with AI/ML engineers, data scientists, product, and design teams to deliver scalable, reliable, intuitive, and responsible AI experiences.

Lead AI solutions development in finance using statistical methods and machine learning. Mentor, implement models, ensure compliance, and collaborate for effective tool integration.

Partner with teams to design, pilot, and scale generative-AI workflows and automation. Build prompt libraries, evaluate AI platforms, integrate APIs, draft executive communications, and drive cross-functional adoption of AI solutions to improve efficiency and decision-making.

Designs, develops, and deploys end-to-end AI/ML solutions for financial services, including generative AI applications, LLMs, RAG systems, data pipelines, and production machine learning models. Builds AWS-based infrastructure and ETL workflows, performs model validation and risk assessments, establishes benchmarking and monitoring frameworks, and ensures compliance with banking regulations, privacy requirements, and ethical standards. Collaborates with technical and business stakeholders to identify AI opportunities and communicate complex concepts.

Designs and deploys AI assistants, copilots, custom agents, and end-to-end workflow automations across business functions. Builds integrations between AI platforms and enterprise systems using APIs, middleware, and serverless architectures. Develops prompt, RAG, and agentic solutions while measuring ROI and adoption. Partners with IT Operations and Security on governance, privacy, compliance, and secure deployment. Documents solutions, trains teams, and continuously improves automations based on usage and feedback.

Own strategic C-suite partner relationships and drive adoption, onboarding, implementation, expansion, and measurable outcomes for Runway’s AI products. Collaborate with creative and technical teams to identify use cases, prototype new opportunities, provide product feedback, and shape product strategy. The role requires technical fluency, executive communication, customer-facing implementation experience, and the ability to operate autonomously in ambiguous, fast-paced environments.

Owns end-to-end technical architecture for Salesforce Data 360 and Agentforce solutions. Designs scalable data models, ingestion and identity-resolution strategies, real-time activation, RAG and generative AI solutions, integrations with MuleSoft and enterprise data platforms, and CI/CD practices. Collaborates with customers and delivery teams throughout the project lifecycle, mentors technical staff, and ensures secure, governed, well-tested deployments.

Serve as the division’s chief AI advisor, leading adoption, integration, governance, and deployment of generative AI, LLMs, machine learning, and intelligent automation across mission-critical defense software. Develop AI and NLP pipelines, dashboards, prototypes, and developer tooling; establish AI-augmented engineering standards; evaluate AI-generated code; mentor engineering teams; and ensure secure, reliable, compliant implementation in sensitive environments.

Serves as the division’s chief AI advisor, leading adoption, integration, governance, and secure deployment of generative AI, LLMs, machine learning, and automation across mission-critical defense software. Develops AI pipelines, dashboards, prototypes, and developer tooling; evaluates emerging frameworks; establishes AI-augmented engineering standards; and mentors engineering teams. The role requires collaboration across engineering, security, compliance, and program leadership, with responsibility for reliable, secure, and mission-assured AI solutions.

Serves as the organization’s chief AI advisor, leading the adoption, integration, governance, and secure deployment of generative AI, LLMs, machine learning, and automation across defense software systems. Develops AI-enabled engineering practices, NLP pipelines, dashboards, prototypes, and production integrations while evaluating emerging developer tools. Mentors engineering teams, defines AI standards, addresses security and compliance risks, and collaborates with technical and non-technical stakeholders to improve delivery speed, reliability, and mission assurance.

Design and deliver scalable software, data engineering, and AI solutions for Barclays’ Liquid Financing business. Build and optimize cloud-based platforms using Python or Java, AWS, Kubernetes, event-driven architectures, and real-time data pipelines. Integrate LLM APIs, embeddings, and retrieval patterns into applications while collaborating with product managers, engineers, quants, and strats. Provide technical leadership, guide architecture and strategy, manage risk and controls, advise stakeholders, and mentor less experienced specialists.

Leads the design, development, and deployment of enterprise AI applications, intelligent agents, LLM platforms, MCP servers, RAG systems, and AI APIs. Manages AI platform capabilities including model routing, governance, observability, and orchestration. Leads and mentors engineers, shapes technical strategy, partners with business and clinical teams, evaluates emerging AI technologies, and presents recommendations to senior leadership. The role requires hands-on Python development and expertise in production AI systems within a regulated biotechnology environment.

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.

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.

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.

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.

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.

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.