Top AI Engineer Jobs

Design and implement AI compiler features and optimization passes, using LLVM/MLIR and related tools. Develop code generation for new hardware, optimize AI workloads, and build AI software in C++ and Python. Collaborate with hardware architects and ML developers, define requirements, estimate effort, and drive coding standards and adoption.

Conduct independent AI research spanning large language models, reasoning, knowledge acquisition, factuality evaluation, and trustworthy AI. Translate research ideas into scalable prototypes, implement and extend state-of-the-art methods, design experiments, develop algorithms and evaluation methods, use AI-assisted development tools, and communicate findings through technical reports and research papers.

Design and build applications using Python, manage component design, optimize performance, and collaborate with clients on innovative products.

Build and operate GPU and accelerator infrastructure across cloud, on-premise, and neocloud environments. Responsibilities include Kubernetes and Slurm orchestration, distributed training, LLM serving, scheduling, benchmarking, observability, automated remediation, cost attribution, autoscaling, and capacity planning. The role writes production code, owns systems end to end, supports on-call operations, and develops tools and dashboards for technical and non-technical users.

Develop and modernize enterprise applications, transforming legacy systems into scalable, supportable architectures. Design APIs, integrations, services, automated tests, and production deployment processes. Build AI-enabled applications, agents, workflows, and assistants using LLM APIs, prompting, tool calling, RAG, and enterprise data integrations. Evaluate AI quality, security, and reliability while implementing monitoring, access controls, human review, and failure handling. Troubleshoot production issues, reduce technical debt, document solutions, and collaborate with cross-functional technical teams.

Build and operate an AI-enabled software delivery platform using Go, Python, Temporal, React, LLMs, retrieval systems, and MCP servers. Own features from design through production, develop APIs and data schemas, maintain knowledge pipelines, improve retrieval quality, support coding-agent workflows, triage production issues, and provide delivery visibility across enterprise integration and ERP programs.

Owns the end-to-end governance review and implementation of enterprise AI products. The role manages submissions through cross-functional Legal, Privacy, Security, Procurement, and technology reviews; coordinates blockers, schedules, documentation, and risk escalation; and leads approved solution onboarding across cloud, Okta, ServiceNow, CSDM, and enterprise technology teams. It also improves governance processes, develops implementation playbooks, and ensures compliant post-launch operations.

Lead the design and deployment of AI platforms, agentic systems, model gateways, RAG pipelines, and AI-focused DevOps infrastructure. Build self-service GCP AI APIs, SDKs, and Terraform modules; manage model routing, observability, security, deployment, and automation using GKE, Cloud SQL, Cloud Build, and related services. The role requires extensive platform engineering, cloud infrastructure, IaC, MLOps, and enterprise AI architecture experience.

Operate and improve an on-premises Kubernetes-based AI and large language model platform across development, QA, and production. Responsibilities include infrastructure deployment, maintenance, troubleshooting, access provisioning, CI/CD support, Ansible automation, monitoring, incident response, documentation, and collaboration with infrastructure, security, and engineering teams.

Owns AI governance submissions from intake through cross-functional review, risk escalation, approval, and compliant implementation. Coordinates Legal, Privacy, Security, Procurement, Cloud, Identity, ServiceNow, and enterprise technology stakeholders; tracks schedules, blockers, documentation, and onboarding requirements. Ensures approved AI products are registered in CSDM, completes post-implementation checks, and improves governance processes, playbooks, and enterprise integrations.

Design, build, deploy, and scale enterprise AI and Generative AI solutions (LLM apps, RAG, agentic workflows). Develop data pipelines, MLOps/LLMOps, cloud deployments (AWS/Azure/GCP), containerized services, and retrieval architectures. Partner with stakeholders, embed responsible AI, and create reusable assets and governance controls.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build and evaluate secure, robust machine learning systems for government sponsors. Responsibilities include researching AI vulnerabilities, developing adversarial ML defenses, designing prototypes, experimenting with ML frameworks and algorithms, testing system performance and security, transitioning capabilities to sponsors, collaborating across technical teams, and mentoring engineers. The role requires onsite work in Pittsburgh or Arlington, up to 25% travel, and an active Department of War security clearance.

Design, build, deploy, and scale enterprise AI and Generative AI solutions (LLM apps, RAG, agentic workflows). Build data pipelines, MLOps/LLMOps, cloud deployments, and reusable frameworks while ensuring responsible AI, governance, and stakeholder collaboration across multidisciplinary teams.

Designs and delivers enterprise data and AI capabilities from concept through production. Responsibilities include defining target architectures, evaluating build-versus-buy decisions, building hands-on data and AI solutions, reviewing code and models, guiding platform adoption, and advising executive stakeholders. The role covers machine learning, generative AI, RAG, agent architectures, semantic layers, MLOps, model governance, cloud data platforms, and production readiness in a regulated environment.

Designs, builds, and productionizes AI-powered applications using LLMs, agentic architectures, retrieval patterns, model APIs, and enterprise AI platforms. Partners with product and engineering teams throughout the development lifecycle, from prototyping through deployment and operations. Develops reusable APIs, SDKs, frameworks, and reference implementations; evaluates emerging AI technologies; resolves complex production issues; and provides technical mentorship and architectural guidance.

Build and lead full-stack web applications and enterprise search capabilities using GCP and Vertex AI Search. Responsibilities include frontend and backend development, API and cloud service design, search indexing and relevance optimization, experimentation, performance tuning, testing automation, observability, reliability, and technical mentorship. The role partners with product, UX, marketing, data science, and ML teams to deliver personalized discovery experiences across GM digital products.

Develop and operationalize AI-native engineering tools, workflows, standards, and reference architectures across the enterprise. Research emerging technologies, build proofs of concept, integrate approved tools, and create scalable engineering patterns. Develop and deliver AI training, tutorials, documentation, presentations, and demonstrations for technical teams and executives. Support adoption through office hours, change management, stakeholder communication, troubleshooting, and feedback gathering.

Execute data mapping, interfaces, integrations, and data-quality tasks across PLM and MDM platforms. Develop, configure, test, and document reusable service-oriented software and data-delivery components. Build proof-of-concepts, support enterprise architecture standards, evaluate tools, collaborate with business and project teams, and address product-structure and systems-integration challenges. The role requires experience with application engineering and exposure to ALM, PLM, microservices, big data, enterprise search, cloud, and DevOps technologies.

Leads enterprise application and data architecture initiatives across PLM and MDM platforms. Defines technical roadmaps, designs service-oriented and reusable software components, evaluates emerging technologies, and ensures data quality across integrated systems. Partners with business leaders, manages vendors, develops business cases, mentors technical teams, and builds proof-of-concept solutions using cloud, DevOps, microservices, big data, and enterprise search technologies.

Build and scale AI infrastructure through Python automation, AWS serverless architectures, EKS containerized workloads, Terraform infrastructure as code, and monitoring. Develop orchestration pipelines for model training and inference, manage large datasets and production availability, reduce recovery times, collaborate with QA and Product, and mentor engineers while maintaining code quality and operational performance.

Build and operate infrastructure for evaluating coding agents and language models. Design end-to-end features spanning data models, APIs, background workers, and UI; run sandboxed trials; score and aggregate results; improve benchmark quality checks; integrate models and agents; and maintain performance, reliability, concurrency, migrations, tests, and code quality.

Lead end-to-end design, development, and delivery of enterprise Generative AI and ML solutions. Architect scalable LLM, RAG, and agent-based systems, drive technical decisions, implement production-ready MLOps/CI-CD, and mentor engineers. Collaborate with product and business stakeholders, ensure monitoring, security, and operational support, and evaluate emerging AI technologies to improve delivery.

Leads advanced testing and validation of aerospace systems and components, including test planning, procedures, reporting, equipment use, and technical problem solving. Mentors early-career engineers, oversees program milestones, represents Test Engineering with senior stakeholders, and serves as a prime technical contact on major programs. Requires expertise in test engineering methods and active Top Secret clearance.

Leads the design, operation, optimization, and governance of cloud, data, and AI platforms. Responsibilities include performance monitoring, incident response, disaster recovery, infrastructure design, automation, CI/CD, Azure DevOps workflows, and Azure Machine Learning and AI Foundry deployment pipelines. The role also establishes operational standards, improves observability, manages complex troubleshooting, supports security controls, documents platform processes, and mentors junior engineers.