Top AI Engineer Jobs

The Senior AI NodeJS Engineer develops AI Agentic solutions, collaborating with teams, managing tasks, ensuring quality code, and staying updated on AI technologies.

Lead architecture and implementation of high-throughput, low-latency AdServer and Exchange services. Design scalable, resilient distributed systems, integrate demand/supply partners, improve RTB/auction capabilities, advance observability and automation (including AI-driven testing/IaC), and mentor engineers to increase platform reliability and revenue growth.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Design and develop AI-powered features for healthcare platforms, build full-stack applications, collaborate with teams, and implement best practices in AI product engineering.

Design and implement AI-powered features for healthcare platforms, develop full-stack applications, and collaborate with stakeholders in a fast-paced Agile environment.

Lead design and delivery of scalable, production-grade agentic AI and automation systems integrated with enterprise collaboration tools (Slack, Outlook, Zoom, SharePoint, Copilot). Drive architecture-to-execution, CI/CD, infrastructure-as-code, monitoring, and cross-team collaboration to scale AI-driven workflows and improve associate productivity.

The Senior AI Software Engineer will lead architectural decisions and drive the development of a scalable Risk Management System, collaborating with various teams to enhance claims management efficiency while implementing AI solutions.

The Senior AI Software Engineer will lead the development of a Risk Management Information System, enhancing claims management through AI and collaboration with various stakeholders. Responsibilities include defining technical roadmaps, providing architectural leadership, and delivering scalable solutions.

Design, build, test, deploy, and operate end-to-end AI/ML pipelines for mission systems. Automate workflows, ensure monitoring and observability, integrate models into production across hybrid cloud/on-prem environments, and collaborate with cross-functional teams while meeting security and infrastructure requirements.

The Full Stack Developer will implement OpenAI and GPT-3 tools to develop processes for creating content and editing media for podcasts.

Build and scale real-time backend systems for AI Agents, including orchestration, memory and context management, low-latency inference pipelines, eval frameworks, event-driven workflows, APIs (REST/gRPC), and production-grade observability, partnering with Applied AI and product teams to deploy multi-tenant, enterprise AI agent platforms.

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.

The AI Sales Engineer will build relationships with customers, lead product demonstrations, handle objections, and act as a domain expert in ML. Responsibilities include collaborating with sales and technical teams to create value propositions and developing educational materials about ML and MLOps.

Research and develop ML and statistical algorithms for networking, edge computing, cybersecurity, and autonomy. Build and evaluate simulation-driven solutions, lead subsystem software development, create automated tests, and contribute to proposals and technical plans while presenting results to customers and senior staff.

Develop and maintain microservices-based applications in an Agile environment, utilizing AI tools and collaborating with systems engineering and delivery teams.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Lead modernization of software quality systems by applying automation and AI to compliance monitoring, SLC oversight, audit and CAPA support. Drive DevOps integration, automated dashboards, metrics, analytics, training, and cross-functional remediation to ensure regulatory readiness and continuous improvement.

Apply machine learning and AI models to real-world problems by designing, building, and deploying software systems. Implement end-to-end model workflows, ingest and transform large datasets, research and test advanced topics, verify software meets requirements, follow QA processes, document findings, and support engineering teams.

Design and produce a multi-module workshop series on Agentic AI for IT Support and Data Analytics: create lesson plans, scripts, demos, and 5–8 minute on-camera videos; build 30–45 minute hands-on workshops and performance-based assessments; translate real job tasks into progressive learning aligned to entry-level roles.

Lead design and engineering of enterprise AI-powered automation using n8n as an orchestration platform. Build scalable, secure API-first and event-driven integrations, AI-enabled workflows and agents, CI/CD and governance, observability, and reusable automation frameworks. Provide technical leadership, mentor engineers, and drive platform standards and operational excellence.

Design, implement, and operate high-performance data center networks optimized for AI/GPU workloads. Collaborate with compute, storage, and security teams, lead incident response and 24/7 operations, mentor engineers, evaluate emerging interconnects and networking tech, and define long-term data center network strategy and standards.

Build and optimize local LLM inference and agent runtimes for Windows and GeForce RTX GPUs. Profile and reduce latency/memory, integrate CUDA/TensorRT pipelines, implement sandboxed security and privacy routers, and harden multi-agent orchestration for constrained consumer hardware while collaborating across research, driver, and open-source teams.

Conduct AI research to develop new approaches, algorithms, and methodologies for portfolio companies; influence technical direction, drive AI system improvements, and collaborate with founders to accelerate growth and impact.

Develop and execute test plans for AI models; identify and analyze defects and anomalies; implement automated evaluation frameworks (dataset checks, API tests, CI/CD); define AI quality metrics (accuracy, latency, fairness, cost); collaborate with development teams to improve model performance; analyze test results and provide actionable recommendations to enhance model accuracy and reliability.