Top AI Engineer Jobs

Designs, tests, optimizes, and manages prompts for large language models and AI applications. Develops reusable prompt templates, evaluates AI outputs, conducts A/B testing, monitors performance, and improves accuracy, consistency, and business relevance. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into prompting strategies. Researches generative AI advancements and contributes to prompt engineering standards, evaluation methods, and responsible AI practices.

Leads AI/ML transformation and applied research across Global Research. Identifies high-value use cases, configures AI-enabled workflows, develops ML solutions, and embeds AI capabilities into products and instruments. Applies advanced methods including Bayesian optimization, surrogate modeling, generative modeling, and data-driven experimental design. Establishes adoption standards, mentors scientists and engineers, leads cross-functional initiatives, and supports training programs across international research sites.

Leads the engineering team building and operating an enterprise AI platform. Responsibilities include platform reliability, security, scalability, cloud infrastructure, CI/CD, deployment automation, observability, governance, developer experience, technical standards, architecture reviews, and mentoring. The role requires production experience with AI or machine learning solutions, cloud-native platforms, distributed systems, infrastructure as code, containerization, platform security, and modern AI components such as vector databases, retrieval systems, orchestration frameworks, and model serving.

Drive organization-wide adoption of AI development standards, tools, and workflows across engineering. Build feedback loops, enablement content, prompt libraries, playbooks, training assets, and adoption dashboards. Partner with engineering leaders to improve AI practices, support onboarding and office hours, measure productivity and quality impact, and communicate progress through technical narratives and success stories.

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.

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.

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.

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.

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.

Develop and maintain AI tooling systems for space stations, focusing on frontend customizations, AI integration, and full-stack feature implementation.

Design, build, and scale an AI-native conversational platform powering autonomous customer agents. Own full-stack systems, retrieval and orchestration, observability, and multi-channel experiences. Lead technical decisions, mentor engineers, and deliver reliable, tested, customer-facing features that enable brands to launch and improve configurable AI agents.

Lead and grow a team building Klaviyo's Customer Agent conversational AI platform. Set technical direction, own roadmap, collaborate cross-functionally, guide system and model design, ensure reliability, hire and develop engineers and managers, and deliver scalable, AI-driven customer engagement features.

Owns AI-assisted engineering across accelerator software teams, embedding agentic workflows into coding, debugging, triage, testing, documentation, CI, and silicon-debug processes. Builds shared agent tooling, maintains evaluations, measures productivity gains, and establishes standards for reliable AI-native development. Requires expert experience with Codex, Claude Code, LLM APIs, evaluations, and adopted developer tooling, plus strong systems or infrastructure credibility.

Own cross-team, platform-level engineering initiatives end-to-end: architecture, migrations, observability, on-call, and customer outcomes. Drive AI-native tooling and agentic AI adoption, mentor senior engineers, and set technical standards across teams.

Build research infrastructure and tooling that enable AI models to design semiconductor chips. Responsibilities include developing reinforcement learning environments, integrating EDA tools, creating evaluation and model harnesses, improving experiment reliability and observability, debugging infrastructure, and converting successful research experiments into reusable systems and training workflows.

Build and operate production-grade AI, data, and distributed systems for fraud intelligence. Responsibilities include real-time data pipelines, high-throughput architecture, LLM-powered agentic workflows, AI tooling, rule and label automation, ML pipeline productionization, privacy safeguards, and cross-functional collaboration. The role requires strong Python engineering, distributed systems, cloud infrastructure, big data technologies, and machine learning fundamentals.

Own and operate the company’s enterprise AI platform across Google Cloud, Claude Enterprise, and Gemini Enterprise. Responsibilities include AI platform operations, CI/CD, infrastructure automation, observability, identity and user lifecycle management, marketplace extensions, internal agents and tooling, AI governance, evaluation, onboarding, documentation, and enablement. The role requires 3–5 years of platform, DevOps, SRE, MLOps, backend, or infrastructure experience and is onsite four days weekly in Chicago.

Build OpenArt’s enterprise AI platform for teams and organizations. Responsibilities include developing workspaces, RBAC, SSO/SAML, audit logging, usage-based billing, invoicing, payment integrations, scalable APIs, multi-tenant architecture, and polished React/Next.js administration experiences. The role also involves improving security, data isolation, reliability, and SOC 2 groundwork; partnering with founders, sales, and customers; making infrastructure decisions; and establishing enterprise engineering practices as the first hire.