Top AI Engineer Jobs

Lead full ML/AI engineering lifecycle: design, data preprocessing, pipeline engineering, model training, deployment, API development, and production support. Configure cloud/on‑prem AI infrastructure (AWS/GCP/graph DBs), mentor engineers, provide technical leadership, and ensure risk/compliance alignment for GenAI and ML solutions.

Design, develop, and deliver production-grade agentic AI systems. Collaborate on AI architecture, integrate models, and optimize workflows. Lead workshops and define solutions with clients.

Design and implement AI-native solutions for defense customers by integrating ML/LLM tools, full‑stack development, cloud platforms, and agentic workflows. Collaborate with engineers, data scientists, and mission experts to deliver secure, production-ready software for the U.S. Space Force.

Design, build, scale, and maintain the companys e-commerce marketplace platform using AI-assisted workflows. Deliver production-quality full-stack features, validate AI-generated code, mentor engineers, improve systems, and partner with Product and Design to execute initiatives.

Design, build, and operate large-scale, low-latency recommendation systems (retrieval, ranking, multi-objective optimization). Drive offline training, online inference, A/B testing, and metric analysis. Apply LLMs and foundation models for content understanding, user intent, and feature generation. Rapidly prototype AI/LLM ideas and own projects from MVP to production, ensuring data quality and system performance.

Lead technical direction and architecture for large-scale personalization and recommendation systems. Drive adoption of LLMs/foundation models, own retrieval-to-ranking pipelines, model lifecycle management, experimentation, and production scaling. Mentor teams and shepherd 0→1 AI platform initiatives into maintainable production.

Design, implement, and evaluate novel AI systems (LLMs, automated reasoning, knowledge acquisition) for assessing journalism. Translate research ideas into scalable prototypes, run experiments, adapt/fine-tune models, develop new algorithms and evaluation methods, and communicate results via reports and papers.

Design, provision, and operate scalable multi-cloud infrastructure and CI/CD pipelines. Deploy and manage containerized workloads, implement monitoring and security best practices, optimize cloud costs, maintain IaC and runbooks, and support incident response and on-call rotations to enable reliable production services and GenAI transformations.

Build end-to-end merchant-facing features using AI-native workflows (Laravel backend, Vue 3 frontend). Prototype, validate with users, adopt AI tooling, ensure quality and testing for AI-generated code, and collaborate with product and design to ship fast.

Lead integration, validation, and performance characterization of Lumai Iris server units; perform system bring-up and on-site troubleshooting in data centres; train operators and act as primary technical contact; feed deployment feedback into product and engineering.

Build end-to-end AI-driven automations across finance, operations, and clinical-support: browser automation for invoices, document data extraction, internal AI agents, data integrations, and fast prototypes. Work directly with the CAIO to turn loose specs into shipped tools.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

The role involves leading the architecture, development, and scalability of fullstack systems for AI agents while mentoring engineers and making technical decisions.

The role involves designing and building fullstack systems for AI agents, along with research and collaboration across teams to improve user experience.

Join the AI team to build automation features in AI/ML, owning projects from concept to integration, and collaborating with leadership.

The role involves designing, building, and scaling AI systems and infrastructure, implementing ML orchestration workflows, and managing production operations for AI services.

Design, build, and deploy AI-powered systems and intelligent agents. Integrate models into applications, develop scalable AI infrastructure, prototype and scale solutions with business stakeholders, and ensure ethical standards. Collaborate cross-functionally to streamline operations and enhance decision-making across the enterprise.

Own end-to-end delivery of a GenAI-powered security deterrence product: design services and APIs, integrate detection models with prompting and voice/TTS, build evaluation and regression suites, ensure precision and safety for real-world actuations, and drive build-vs-buy decisions while collaborating across platform, data, and ML teams.

Design, build, and maintain MLOps software systems and ML pipelines; deploy, test, and monitor AI/ML models on cloud-native platforms; implement ETL and data workflows; ensure model performance, security, and scalability; lead technical roadmap, mentor engineers, and evaluate emerging MLOps tools.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.

Designs and integrates approved AI/ML tools into intelligence workflows by evaluating and configuring tools, redesigning processes, building prompt libraries and templates, measuring performance with KPIs, applying Lean Six Sigma improvements, and driving analyst adoption through training and stakeholder collaboration.

As a Product Engineer, you'll collaborate on AI solutions for expert sourcing, client portals, and autonomous agents, driving product vision and coding.

Design, build, and scale the AI platform for distributed multi-GPU training, experiment tracking, and model deployment. Optimize GPU clusters, networking (NCCL/InfiniBand), Kubernetes-native scheduling, and inference serving (TensorRT, Triton). Develop agentic automation for cluster health, CI/CD, and data curation while integrating ML lifecycle tooling and observability.

Develop Python-based ML components for spatial data quality, design robust software, and collaborate in an Agile environment.

The Software Development Engineer II focuses on integrating AI technologies into geospatial software, developing tools for automation, and collaborating with teams for innovative features.