Top Artificial Intelligence Jobs

Design, build, and deploy production-grade AI applications end to end. Develop stateful multi-agent workflows with LangGraph, rapidly prototype full-stack applications, and create automated LLM evaluation systems measuring accuracy, latency, hallucinations, and regressions. Integrate AI safely into business workflows, work with vector and relational databases, provide framework feedback, and mentor teammates while owning applications from concept through production.

Build prototypes and utility tools using GenAI and open-source LLMs, evaluate low-code and no-code automation platforms, and identify business-process automation opportunities. Translate business needs into technical requirements and user stories, support AI-enabled workflows, train internal teams, and document tools and processes. The role bridges business and technical teams while improving operational efficiency across Medscape Education.

Own the strategy, discovery, prototyping, productization, and scaling of AI-powered commercial offerings. Develop supporter-facing workflows, content customization, reporting, analytics, and campaign performance capabilities. Partner with sales, marketing, clients, platform product, AI/ML, engineering, and data science teams to create scalable and monetizable products. Track adoption, revenue contribution, efficiency gains, and AI feature impact while ensuring responsible, reusable platform capabilities.

Design, build, evaluate, and deploy production-grade AI applications end to end. Develop stateful multi-agent workflows with LangGraph, rapidly prototype full-stack MVPs, and create automated LLM evaluation systems measuring accuracy, latency, hallucinations, and regressions. Integrate AI safely into business workflows, work with vector and relational databases, provide framework feedback, and mentor teammates while balancing technical complexity with intuitive user experiences.

Develop and deploy AI-powered internal and client-facing solutions using generative and agentic AI. Assist with design documentation, prompt engineering, LLM integrations, testing, troubleshooting, deployment, monitoring, and production support. Collaborate across teams while learning DevOps practices and contributing to organization-wide AI automation initiatives.

Designs, develops, deploys, and maintains secure AI-agent and automation solutions for enterprise and client operations. Builds LLM workflows, backend integrations, AWS serverless infrastructure, event-driven pipelines, database connections, monitoring, and deployment systems. Translates ambiguous business processes into reliable, validated, auditable solutions with human approval controls. Participates in stakeholder discovery, technical documentation, testing, deployment, incident resolution, and continuous improvement.

Lead and define AI architecture, ensuring scalable, reliable AI systems while mentoring engineers and collaborating with product teams.

Build and own analytics and AI-driven reporting for Care Partnerships. Create self-service insights, predict partner health, prioritize outreach, instrument experiments and launch analytics infrastructure to scale referrals and partner relationships.

Develops AI applications supporting defense, cyber, intelligence, and mission environments. Responsibilities involve programming with Python or JavaScript/TypeScript, working with vector databases, embeddings, model APIs, evaluation frameworks, agent testing harnesses, model hosting, inference pipelines, and cloud AI platforms. The role also requires secure AI development, data protection, human-in-the-loop workflows, AI assurance, and evaluation methodologies in government settings.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Build and lead internal AI tooling and automation for security: develop threat-hunting and remediation tools, own large-scale log and ETL pipelines, maintain Kubernetes and serverless infrastructure, partner with product teams to reduce risk, and mentor junior engineers.

Design and operate scalable, event-driven backend systems in Python to power agentic AI workflows and real-time financial infrastructure. Own APIs, data architecture, reliability primitives, and platform foundations while partnering with ML and product teams to productionize AI-native financial systems.

As a Knowledge Development Engineer, you'll architect and deploy AI-driven production systems, reverse-engineer existing systems, and guide product development. You'll synthesize customer feedback and lead experiments to connect technical development with business outcomes.

The Knowledge Development Engineer leverages AI agents to solve customer problems by building applications and automating workflows, focusing on delivery speed and quality.

Design, build, and deploy production AI agents and orchestration systems using tools like LangChain and LlamaIndex. Apply prompting, RAG, fine-tuning, and model selection. Architect agent reasoning and workflows, implement monitoring and evaluation, and integrate AI systems with backend and frontend services for scalable, reliable, compliant production deployments.

Design and operate scalable, event-driven backend systems in Python for AI-native financial products. Build typed APIs (FastAPI + Pydantic), own data and ledger architectures, develop reliability primitives, partner with ML teams to productionize models, and ship platform foundations for audited, high-throughput financial workflows.

Build and lead internal AI-driven security tooling and automation, support incident response with threat-hunting tools, maintain Kubernetes/serverless and ETL/log pipelines, partner with product teams to reduce risk, and mentor engineers.

Design and deploy agentic AI systems that automate multi-step financial workflows. Architect agents using frameworks like LangChain and LlamaIndex, apply prompting, RAG, and fine-tuning, optimize reasoning and tool use, build feedback-driven systems, and integrate AI reasoning securely into production backend and frontend services.

Lead product strategy and corporate development for AI initiatives at Datadog. Define AI product direction, evaluate market opportunities, partnerships, and M&A, collaborate with cross-functional teams, and support go-to-market and roadmap decisions to accelerate AI capabilities across the platform.

The Senior AI Engineer will build and scale AI features, develop AI agents, and partner with product teams to enhance products for small businesses.

As an AI Engineer, you will create a unified evaluation infrastructure for AI agents, ensuring reliable performance while integrating automated pipelines and feedback systems for rapid model evaluation and quality assurance.

Lead and conduct ML research aligned to Sprinter Health's strategy: define research agenda, design rigorous experiments, develop novel methods and evaluations, publish and patent findings, translate research into production-ready tools, and collaborate with clinicians and cross-functional teams to validate and deploy clinically robust AI solutions.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Lead the AI research agenda at Kepler, focusing on trustworthy AI for enterprise decisions. Oversee research on agentic systems and evaluation frameworks, and manage a research team. Ensure production deployment of innovative AI solutions based on real financial data.