Top AI Engineer Jobs

Own the company’s AI intelligence layer end to end, including reasoning, learning loops, evaluations, guardrails, and reliability. Build and deploy applied LLM systems for real-world users, establish the AI architecture, identify and address failure modes, and eventually hire and lead the AI team. The role emphasizes production AI judgment and systems building over model research or industry experience.

Design, build, and deploy AI/ML and generative AI solutions for internal teams and biopharma clients. Integrate AI APIs, create prototypes and production applications, develop data pipelines, and deploy scalable cloud-based systems. Build client-facing tools, dashboards, and intelligent services while evaluating emerging AI technologies. Collaborate with cross-functional teams, provide technical guidance, gather requirements, support platform rollouts, and promote responsible AI, LLM, RAG, prompt engineering, and agent best practices.

Leads and develops a team building AI enablement tools, developer tooling, reusable accelerators, automation, and shared engineering systems. Remains hands-on with coding, code review, prototyping, and AI-assisted development. Drives production adoption of AI through evaluation, governance, observability, resilience, and operational best practices. Establishes ownership in ambiguous environments and influences architecture, strategy, and infrastructure investment across engineering, security, product, and leadership teams.

Leads and mentors an engineering team building AI enablement platforms, developer tooling, automation, reusable accelerators, and shared engineering systems. The role remains hands-on with coding, code review, prototyping, architecture, production AI adoption, evaluation, governance, observability, resilience, and operational improvement. It partners across engineering, security, product, and leadership to define ownership, strategy, infrastructure investment, and measurable adoption outcomes.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.

Build and scale production-grade AI and generative AI systems on AWS, including LLM applications, RAG pipelines, machine learning models, data pipelines, APIs, and MLOps workflows. Deploy, monitor, version, and retrain models while implementing responsible AI guardrails, governance, security, and compliance. Provide technical mentorship and collaborate with architects and business teams across insurance platforms.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Designs, develops, and deploys AI-driven applications and workflows on ServiceNow. Gathers requirements, integrates machine learning, NLP, predictive analytics, and external systems, and manages application coding, testing, debugging, documentation, deployment, and incident remediation. Implements ServiceNow modules, CMDB, Discovery, workflows, service maps, and event management while collaborating with stakeholders and technical leads. The role also includes mentoring engineers, supporting complex professional services projects, and maintaining platform standards.

Lead the development of AI-driven test automation frameworks for cloud-native systems, focusing on quality engineering and mentoring.

Lead enterprise strategy and architecture to make data AI- and agent-ready. Define standards for metadata, semantic layers, governance, RAG/vector retrieval, agent orchestration, APIs, and DevSecOps to embed Agentic AI across data engineering and platforms while influencing stakeholders and driving adoption.

Build secure, scalable platforms and automation for cybersecurity operations, including threat hunting, incident response, PSIRT, product security, vulnerability management, and analyst workflows. Develop LLM-powered agents, integrate SIEM, SOAR, cloud, endpoint, ticketing, and scanning systems, and process high-volume security telemetry. Partner with security teams to automate investigations, enrich alerts, prioritize risks, track remediation, and deliver reliable production services.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Own end-to-end delivery of core product features, build AI/ML pipelines for construction document and drawing analysis, and shape scalable frontend, backend, and ML architecture. Collaborate with co-founders and construction users, translate workflow needs into product improvements, establish engineering practices, and help grow the team. The role spans computer vision, LLM systems, frontend, backend, and production infrastructure in a fast-moving startup environment.

Design and lead agentic AI workflows for biologics discovery, automating secondary assay analysis and connecting lab automation, scientific data, models, and compute. Build real-time pipelines, resilient APIs, workflow orchestration, observability, and production integrations. Enable closed-loop design-make-test-learn feedback through high-quality, traceable, AI-ready data. Partner with discovery, data science, GenAI, ontology, MLOps, and infrastructure teams, while setting technical direction, evaluating emerging technologies, and mentoring engineers.

Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.

Lead design, development, deployment, and support of foundation model training and LLM inference platforms. Build similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, and scalability; contribute to technical vision and roadmap while partnering across engineering, research, product, and program teams.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

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.

Lead design and deliver multi-cloud AI/ML solutions that ingest and correlate telemetry (metrics, logs, traces), build predictive anomaly detection and forecasting models, automate closed-loop remediation, integrate with ticketing systems, and provide self-service or conversational interfaces to reduce MTTR and ticket volume.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps capabilities, CI/CD pipelines, infrastructure automation, observability, monitoring, governance, and AI deployment services. Support production machine learning and generative AI workloads, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

Design, implement, and deliver AI-enabled product features (LLMs, agents, ML). Build cloud-native services, ensure observability, reliability and scalability, troubleshoot production issues, participate in architecture reviews, and collaborate with product, data, and engineering teams.

Design, build, operate, and scale enterprise AI platforms across public and private clouds. Develop MLOps, infrastructure automation, CI/CD, observability, governance, deployment, monitoring, and lifecycle-management capabilities for machine learning and generative AI workloads. Support production platforms, troubleshoot incidents, improve reliability and performance, collaborate with engineering, security, and governance teams, evaluate emerging technologies, and mentor peers.

The Staff Forward Deployed Engineer at GitLab works with strategic accounts to adopt GitLab and GitLab Duo Agent Platform, guiding technical discovery and building reusable solutions for complex enterprise environments.