Top AI Engineer Jobs

The AI Engineer will build AI-powered solutions to enhance productivity and automation across departments, collaborating closely with leadership and managing integrations with existing SaaS platforms.

Build and ship AI-enabled coaching product features from 0-to-1; design application capabilities on top of foundational models; translate coaching workflows into AI agents; build data pipelines and integrations; and educate the team on data and AI best practices.

Designs and maintains AI-powered full-stack applications using React, TypeScript, Python, and FastAPI on Azure. Builds cloud-native, containerized solutions, data pipelines, CI/CD workflows, Terraform infrastructure, dashboards, and secure production systems. Integrates LLMs, AI agents, and machine learning models while partnering with data scientists and business stakeholders. Provides technical leadership, establishes engineering standards, mentors team members, and manages the full software development lifecycle.

Lead technical direction for cloud platform engineering: design, build, and operate secure, scalable platforms supporting AI/ML and data initiatives. Drive DevEx, CI/CD, DevSecOps, observability, self-service developer workflows, and AI-assisted development including agent and MCP integrations.

Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans, validate GPU and hardware-software interactions, and troubleshoot failures in large-scale distributed clusters. Collaborate with systems engineers using telemetry and profiling tools to identify root causes and performance bottlenecks. The role requires Python automation, test framework development, containerization, and CI/CD experience, with Kubernetes, infrastructure automation, networking, and firmware testing experience preferred.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

Leads development and deployment of computer vision and AI/ML systems for real-time object tracking, monitoring, and intelligent data triage. Designs scalable architectures, agentic data-collection workflows, and production MLOps pipelines; mentors engineers, oversees data lifecycle and model deployment, and translates client needs into AI/ML roadmaps. Requires advanced Python, computer vision, Docker, Kubernetes, seven-plus years of machine learning experience, U.S. citizenship, and an active Secret clearance or higher.

Supports assembly, integration, testing, and final assembly of Blue Ring spacecraft. Translates engineering drawings and configurations into manufacturing processes and work instructions, resolves floor issues, manages nonconformances, reviews drawings, performs FMEA, drives DFMA improvements, and coordinates with design, test, quality, and manufacturing teams to ensure vehicle readiness and configuration control.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Lead prompt engineering for LLM-based solutions: design, test, and refine prompts; collaborate with domain experts and developers; integrate LLMs into applications; build a scalable prompt framework and knowledge base; analyze user feedback and guide tool/platform selection.

Develop and optimize LLM prompts, build scalable prompt-engineering frameworks, integrate AI solutions into applications, analyze user feedback, document best practices, and collaborate with domain experts and software developers. The role also contributes to LLM technology and platform decisions across GE Vernova business units.

Design and optimize prompts for LLMs, collaborate with domain experts to translate business needs, integrate LLM solutions into applications, build a scalable prompt-engineering framework, gather and analyze user feedback, document best practices, and evaluate LLM tools and platforms to improve prompt effectiveness and reuse across business units.

Build and maintain scalable Python-based AI applications under senior guidance. Assist with requirements, feature implementation, data modeling using Pydantic, code reviews, troubleshooting, testing, and CI/CD. The role requires collaboration, communication, and willingness to learn modern Python tools, frameworks, and libraries. Candidates should understand object-oriented programming, REST APIs, large language models, and AI coding tools. The position is fully remote within the United States.

Design and architect the core distributed platform powering production AI agents: orchestration, execution, memory, tool-calling, workflows, reliability, observability, and developer experience. Drive architecture decisions, set engineering standards, mentor engineers, and lead cross-functional initiatives to translate AI capabilities into production-ready platform features.

Own end-to-end development of AI product demos, including React and TypeScript interfaces, streaming chat and agent workspaces, backend-for-frontend services, database schemas, deployment, observability, and on-call. Partner on app-builder tooling, design systems, agent-facing CLI and deployment experiences, accessibility, localization, audit trails, and enterprise readiness. Debug across backend and Kubernetes infrastructure while delivering reliable, AI-native product experiences.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

Own the AI inference and runtime platform, including open-weight model serving, Kubernetes operators and autoscaling, GPU infrastructure, stateful vector and graph stores, and secure microVM-based agent sandboxing. Build Rust, Python, and Kubernetes control-plane services; manage infrastructure as code, observability, metering, lifecycle operations, upgrades, backups, and failover. Establish security controls, open-source standards, and reliable production operations across cloud and managed GPU environments.

Design, build, and ship production agentic AI systems and retrieval-backed LLM features. Automate SME workflows, own evaluation and golden datasets, enforce safety/compliance (e.g., COPPA/FERPA), provide documentation/runbooks, and create internal libraries and playbooks so other teams can adopt AI features.

Develop, implement, and validate production-level control and learning algorithms (MPC, RL, IL) for real-world embodied robotic systems. Design experiments to improve robustness and adaptability, integrate AI control stacks with perception and systems engineering, and use simulation and digital twins to accelerate deployment.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

Lead the AI team, develop AI solutions for semiconductor processes, enhance tool operations, and mentor engineers. Define strategies and architectures for AI integration.

The Applied AI Engineer collaborates with clients to define AI strategies, identifies high-impact use cases, and builds production-ready AI systems, focusing on customer needs and delivering tangible business value.

Seeking a Senior AI Engineer to develop AI solutions, oversee project delivery, and collaborate on system architecture. Requires 5+ years with Python and AI technologies.

Design and build scalable AI infrastructure, automate workflows, optimize GPU workloads, and ensure reliability for AI systems at Eloquent AI.