Top AI Engineer Jobs

Design, build, deploy, and support AI/ML models in production using healthcare data. Ensure data engineering best practices, follow Responsible AI and governance, collaborate with stakeholders, work in Agile/Scrum, and support large-scale healthcare or public sector projects. Must obtain Public Trust clearance.

Design and implement AI/ML solutions for federal healthcare programs using healthcare datasets (Medicare/Medicaid/claims/clinical). Work with Python, SQL, cloud platforms, interoperability standards (FHIR/HL7), ensure HIPAA and federal compliance, and apply LLMs, RAG, and NLP techniques.

Develop, integrate, test, deploy, and maintain production machine learning software and services. Build data pipelines, model training workflows, and inference systems; evaluate scalability, accuracy, and performance; monitor and troubleshoot deployed models; and transition prototypes into production. The role also supports automation, documentation, CI/CD, platform integration, and collaboration across technical teams in secure government-focused environments.

Designs, implements, and hardens AI solutions across OneStream’s finance and analytics platform. The engineer delivers customer-facing implementations, diagnoses field issues, develops reusable demos and workflows, supports production readiness, and collaborates with Engineering, Delivery, Partners, Presales, and customers. The role requires translating technical concepts into business value for finance stakeholders, facilitating workshops, and contributing feedback to product roadmaps. Up to 25% travel may be required.

Design, build, and optimize SpaceX’s high-throughput AI inference platform. Responsibilities include distributed model serving, GPU and inference-engine optimization, request routing, autoscaling, caching, observability, CI/CD, SDK development, reliability, and production performance tuning. The engineer will collaborate across AI infrastructure teams and support internal training workloads. This is an onsite role in Palo Alto.

Sets WGU’s enterprise AI operations and platform strategy, defining architectures, governance, reliability, security, observability, and responsible AI standards. Leads high-impact AI platform and migration programs, advises executives, evaluates emerging technologies, builds engineering culture and teams, mentors engineers, partners with legal and compliance, and represents the organization through conferences, publications, and strategic partnerships.

Drive organic growth and visibility by combining SEO, content strategy, and AI. Optimize content for search engines and LLMs (ChatGPT, Gemini, Claude), implement schema/structured data and entity-based SEO, use SEO tools (GSC, GA, Ahrefs, SEMrush) to measure and iterate, and develop strategies to surface client content in AI assistants and Google SGE.

Provide hands-on legal engineering support to DACH customers using AI workflows: troubleshoot technical issues, create Help Center content, track adoption/ROI, and enable scalable AI adoption for legal teams.

Provide hands-on legal engineering support for AI-driven legal workflows: troubleshoot self-serve customers, create documentation, track adoption/ROI, escalate technical issues, and enable scalable AI adoption for legal teams.

The Azure AI Applications Engineer will lead the development and implementation of intelligent applications using Azure AI services, while architecting solutions, mentoring junior engineers, and collaborating on project delivery across multiple sectors.

Design and build AI-powered tools to accelerate incident detection, triage, root-cause analysis, and automation for Credit trading production. Partner with support, development, and AI teams to embed AI into workflows, maintain high availability, analyze logs/metrics, and measure reductions in MTTR and operational toil.

Build and maintain end-to-end mobile trading systems for iOS and Android. Ship production-quality, well-tested code, apply blockchain and crypto knowledge, and integrate AI tools to improve engineering workflows. Break down projects, collaborate cross-functionally, conduct and act on code reviews, mentor teammates, and participate in hiring and onboarding. Influence product decisions through data-driven insights and provide domain expertise for mobile trading initiatives.

The AI Software Engineer will develop and deploy AI-powered solutions, build MLOps pipelines, integrate data sources, and ensure model performance in a logistics context.

Build and own the production improvement loop for LLM-powered GTM agent workflows: instrument behavior, define evaluation and monitoring, diagnose failures, design and ship behavior improvements, build backend services and feedback pipelines, run experiments and staged rollouts, and partner with product, data, and GTM teams to measure business impact while ensuring safety and reliability.

Lead architecture and implementation of AI-first backend and orchestration systems for Audit Suite. Drive scalable, reliable AI workflows (LLM integration, retrieval, vector stores), mentor engineers, set technical direction, and collaborate across product, design, and ML to deliver auditable, high-throughput document-centric solutions.

Develop AI capabilities using Microsoft tools, integrate AI solutions within enterprise systems, ensure compliance and security, and collaborate with team members for continuous improvement.

The role involves designing and deploying AI/ML models, collaborating with stakeholders, and enhancing operational efficiency through innovative solutions.

Design and operate multi-petabyte, high-performance storage for large-scale AI/HPC clusters. Architect parallel filesystems and object stores, build Kubernetes-native storage operators, optimize RDMA/InfiniBand and NVMe-based data paths for 10–50 GB/s per node, implement multi-tier caching and prefetching, ensure observability and 99.9%+ uptime, and collaborate with ML/SRE teams while driving cost and performance improvements.

The role involves developing scalable backend systems in Go and other languages, building high-volume analytics, and designing APIs for ML workflows.

As a Senior AI Product Engineer, you will build scalable services and front-end applications, design APIs, and enhance AI product offerings, focusing on improving user experience and collaboration with engineering teams.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Design and implement server-side logic and RESTful APIs, manage MongoDB and Redis databases, ensure security and performance, contribute to system design and architecture, review code and scope deliverables, collaborate with Product and Client Success, and maintain documentation.

Design, build, and productionize Applied AI foundations: model access, retrieval/memory, observability, secure data paths, human-in-the-loop workflows, and reusable AI-driven platform capabilities that support product and operational teams.

Embed with core operational teams to map workflows and remove manual work by building production AI agents, copilots, and internal tooling. Deploy retrieval, memory, and evaluation systems to improve reliability, automate underwriting and claims workflows, extract structure from messy healthcare documents, and measure operational leverage through faster workflows and reduced manual processes.

Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.