Top AI Engineer Jobs

The AI Optimization Engineer will apply advanced optimization and AI techniques to decision systems, collaborate with experts, and present findings clearly.

Design, build, and operate on-prem, cloud-like datacenter infrastructure for AI/ML workloads. Own compute, storage, networking, IaC, observability, container orchestration, HashiCorp identity/security stack, and datacenter hardware support. Automate operations, create runbooks, participate in on-call rotation, and work within an SDLC/Scrum process.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

The role involves building and evolving the developer platform, focusing on CI/CD automation, developer tooling, and improving observability and operational excellence.

Lead end-to-end customer engagements to discover, scope, deploy, and realize value from AI agents. Run executive workshops, design and configure agentic solutions, create deployment playbooks, partner on integrations, and drive measurable business impact while shaping product direction.

Lead presales for Data Analytics/AI solutions, secure technical closes, scope and design solutions with sales and delivery, hit productivity quotas, coach sales associates, manage partner relationships, and drive product growth and customer satisfaction.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

Designs, trains, evaluates, deploys, and monitors scalable AI/ML models and data pipelines to meet federal customer requirements. Leads model architecture, testing, integration, and iterative improvement while ensuring data and model quality and presenting results to stakeholders.

Design, build, and deliver AI-driven Salesforce Agentforce agents and intelligent automations. Develop integrations, test automations, debug Apex/JavaScript/Python code, produce technical documentation, mentor junior engineers, participate in DevOps and QA, and support transformational projects and high-priority incidents.

Operate and maintain AWS cloud infrastructure and Kubernetes (EKS) clusters, troubleshoot production incidents, improve CI/CD and infrastructure-as-code (Terraform/Ansible), participate in on-call rotation, optimize capacity and cost, and integrate AI-driven tools for incident triage, root cause analysis, and automation to improve observability and MTTR.

Design and prototype state-of-the-art visual encoders and multimodal representations; train billion-scale models on GPU clusters; build visual reasoning and retrieval tools; collaborate on text-to-image and VLM projects; mentor junior researchers; publish research and ship models to production.

Build and ship core backend and platform systems that integrate AI agents and frontier models to automate staffing workflows. Own architecture, real-time matching, autonomous vetting pipelines, and backend performance, reliability, and scalability. Partner with leadership to set technical standards and iterate the roadmap across full-stack and infrastructure priorities.

Build Traba's product by applying frontier models and AI agents across the stack to automate vetting, matching, and fulfillment. Lead frontend and backend development, architect real-time matching and autonomous vetting pipelines, and design distributed systems and APIs. Own product surfaces end-to-end, mentor engineers, and partner with CTO, product, and design to ship scalable, reliable automation for the staffing marketplace.

Design and build distributed systems for AI workloads, improve reliability and efficiency, and develop infrastructure automation tools.

The AI Solution Engineer executes analytics workflows, maintains data pipelines, monitors ML models, and supports supply chain data ingestion on a hybrid work model.

Develop, test, debug, and maintain microservice-based applications; implement features and integrations using Java, Spring Boot, TypeScript/React, Node.js and AWS; participate in incident resolution, impact analysis, and team delivery practices; document work, monitor system health, and continuously expand technical skills.

Build and deliver enterprise Generative AI platform services, APIs, microservices, and reusable frameworks. Implement CI/CD, automated tests, model deployment, inference pipelines, observability, and scalable secure solutions while collaborating with architects, data scientists, and stakeholders.

Lead the design and implementation of a unified semantic layer and enterprise data models, transforming SAP, Workday, Salesforce, Anaplan and other sources into AI-ready datasets. Establish modeling standards, testing, observability, and automation; collaborate with engineers and business stakeholders to deliver scalable, high-performance data solutions for AI agents and analytics.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

Lead MLOps/DevOps Engineer to design, build, and maintain scalable ML and data pipelines, automate cloud infrastructure (preferably AWS) with IaC, containerize and manage Kubernetes/ECS, implement CI/CD and observability, operationalize models, monitor drift and versioning, and ensure secure, cost-efficient production deployments for Generative AI initiatives.

As a founding Solutions Engineer, you will lead technical discovery, run POCs, and ensure successful customer integration while collaborating with product and revenue teams.

Lead design and implementation of scalable AI-centric systems for offensive security. Develop agentic AI capabilities for vulnerability identification and automated pentesting, own feature delivery from ideation to production, mentor engineers, run design/code reviews, and collaborate cross-functionally to integrate AI into platform systems.

Design, build, and deploy LLM-powered AI agents and intelligent systems for cloud-native, microservices architectures. Partner with stakeholders to create agent-driven workflows, perform prompt engineering, RAG and vector DB integration, orchestrate agents, ensure CI/CD and code quality, manage technical debt, and benchmark/evaluate models to scale responsible AI across investments technology.

Lead and drive strategy for generative AI projects, develop cutting-edge models, engineer data pipelines, and collaborate across teams to implement AI solutions.

Lead the Generative AI team at Xometry, setting vision and strategy, developing multimodal models, and collaborating with various stakeholders to deliver AI solutions.