Top Artificial Intelligence Jobs

Own a financial product vertical end to end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Incorporate KYC, transaction monitoring, privacy, licensing, and other regulatory requirements. Apply AI with evaluation, safeguards, human oversight, and reliability controls while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Build and own an AI-native digital banking business, defining its proposition, roadmap, business model, and AI-powered customer experiences. The CEO will own P&L, customers, deposits, revenue, unit economics, and operating costs; collaborate with product, engineering, and AI teams; build the core team; establish banking and operational capabilities; and lead partnerships, regulatory readiness, and market expansion.

Lead and build BJAK’s AI-native finance business, defining product strategy, roadmap, business model, and new financial offerings. Own P&L, growth, revenue, unit economics, and operating performance while partnering with product, engineering, and AI teams. Build cross-functional teams, launch and scale consumer fintech products, and expand into new markets.

Lead and build an AI-native consumer investing business, defining its proposition, roadmap, business model, and investment products. Own P&L, assets, customers, engagement, revenue, and unit economics while developing AI-powered experiences for investment discovery, research, portfolio management, and decision-making. Partner with product, engineering, and AI teams; build investing, technology, product, and commercial teams; and expand into new markets and investment products.

Design and implement AI agents for mission planning and orchestration across autonomous systems, focusing on LLM frameworks and edge computing.

The Senior Software Engineer will explore AI solutions in healthcare, having experience in full-stack/backend development and startup culture.

As a Senior Software Engineer at Whatnot, you'll build AI tooling for internal use, developing workflows and infrastructure to enhance software delivery. You'll collaborate across teams to gather feedback and prioritize features in a rapidly evolving landscape.

The AI Solution Architect designs enterprise architectures for AI solutions, ensuring they align with business objectives and technology standards while guiding implementation and governance.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

The Senior Manager of Embodied AI will oversee AI/ML engineering teams, manage operational excellence in data scaling for autonomous vehicles, and collaborate across teams to improve performance and innovation in AV technology.

Design, prototype, and deploy generative AI solutions (LLMs, RAG, agents) across the full lifecycle. Build scalable backend services and APIs, deploy models in cloud (AWS/Azure/GCP), collaborate with cross-functional teams, and support knowledge sharing and mentoring.

Design, prototype, and productionize generative AI solutions (LLMs, RAG, agents). Build scalable backend services and APIs, deploy on cloud (AWS/Azure/GCP), collaborate cross-functionally, and support knowledge sharing and mentoring.

Design and lead Replit’s AI agent security architecture, including runtime guardrails, semantic firewalls, sandboxing, threat modeling, red teaming, identity delegation, data isolation, observability, and auditability. Establish secure design patterns, SDKs, risk-register contributions, and compliance documentation for agentic systems. Partner with engineering, infrastructure, AppSec, GRC, executives, and sales to address AI security risks involving prompt injection, tool misuse, data poisoning, context contamination, and autonomous code execution.

Own technical strategy and customer outcomes for public sector AI adoption. Lead discovery, architecture, evaluations, prototypes, security and governance planning, deployment readiness, adoption roadmaps, and production impact across ChatGPT Enterprise, the OpenAI API, Codex, and agentic solutions. Partner with executives, engineers, sales, product, security, and delivery specialists while translating mission priorities into scalable, responsible AI implementations.

Build and maintain scalable full-stack web applications across front-end and back-end systems. Develop RESTful APIs, integrate AI and machine-learning models using Python, manage SQL and NoSQL databases, and create GitHub Actions CI/CD pipelines. Collaborate with data scientists, troubleshoot and improve existing software, participate in code reviews, and apply modern DevOps, cloud, containerization, and microservices practices.

Leads software engineering efforts for production applications, including development, testing, deployment, monitoring, security, resilience, and performance tuning. Designs reusable code and architecture documentation, collaborates with product teams, supports agile delivery, and mentors junior engineers. Contributes to technical roadmaps, cross-functional initiatives, platform adoption, system modernization, and AI-driven engineering productivity improvements.

Designs and implements AI solution architectures that address supply chain and procurement business needs. Builds and ships Python prototypes, develops GenAI applications using messy real-world data, and evaluates AI feasibility, value, risks, and build-versus-buy decisions. The role requires independent problem-solving, technical leadership, ethical AI practices, and mentoring of team members.

Build internal software and AI-powered systems that connect Product, Engineering, Sales, Marketing, Programs, and Operations. Responsibilities include integrating SaaS tools, APIs, databases, LLMs, and AI agents; automating workflows; creating systems that translate product specifications into engineering work; maintaining synchronized documentation; supporting controlled government proposal workflows; and improving how employees and agents collaborate.

Leads enterprise technology solutioning across business development, presales, architecture, and delivery governance. Builds CXO relationships, translates business challenges into scalable AI and application modernization solutions, develops business cases and roadmaps, supports RFP/RFI responses, and guides complex multimillion-dollar pursuits. The role requires enterprise architecture expertise, cloud and integration knowledge, financial modeling, innovation leadership, and experience closing deals worth $15 million or more.

Design, build, and deploy enterprise-grade autonomous AI services and multi-agent systems. Implement MCP and A2A integrations, architect hybrid control flows and state machines, integrate sandboxed tool execution with strict egress controls, and apply SRE principles for observability, fallback mechanisms, and reliability to move generative AI from POC to production.

Partner with business leaders to define AI strategy, set KPIs, and deliver executive reporting; prototype AI solutions (e.g., Google AI Studio); remove operational obstacles across ServiceNow, Jira, and automated workflows; enforce data governance and privacy to enable enterprise AI adoption and measurable ROI.

Lead and grow a team of Agentic AI engineers to transition multi-agent and generative AI POCs into scalable, secure, production-ready services. Define technical roadmap, guide architecture for scalability and cost, champion agent interoperability standards, and establish CI/CD, observability, and delivery practices for LLM-powered applications that drive business impact.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Lead definition and standardization of next‑generation wireless system architecture across 3GPP, GSMA and TM Forum. Drive AI/ML and autonomous network integration, represent the company in standards bodies, define AI observability and agent frameworks, and produce actionable standards and architecture guidance for cloud‑native core networks and future 6G services.

Lead AI product strategy for enterprise shared services, prioritize and govern AI requests, and drive deployment of generative AI/LLM and ML solutions. Manage cross-functional teams to convert manual processes into automated workflows, build reusable AI components and datasets, and deliver measurable operational efficiency and cost reductions across internal systems.