Top AI Engineer Jobs

Design, deploy, operate, and scale GPU/CPU infrastructure for classified AI missions. Build automation for on-premise Kubernetes clusters and operating systems, manage databases, monitoring, storage, and GPU-as-a-service platforms, and improve service reliability and availability. Collaborate with AI engineers and customers to deliver scalable, maintainable infrastructure while supporting deployments in Top Secret data centers.

Design, deploy, operate, and optimize high-speed data center networks supporting AI clusters and national security missions. Responsibilities include optical and copper connectivity, network automation, capacity planning, security, performance monitoring, troubleshooting, hardware integration, firmware updates, and reliability improvements. The role collaborates with ML, hardware, software, and network engineering teams and requires Top Secret clearance, on-call availability, extended hours, and future domestic and international travel.

Build and deploy LLM-powered agents, skills, and platform capabilities across the company. Own the full lifecycle from design and implementation through evaluations, rollout, and ongoing improvement. Define specifications and measurable quality evaluations, partner with the Applied AI product manager, make independent product decisions, and collaborate with sales, revenue operations, editorial, and people operations to identify and automate workflows.

As a Senior Staff AI Engineer, define multi-year technical strategy, architect platforms for voice-based AI in healthcare, mentor engineers, and represent the company in external discussions.

Leads SSD enablement, qualification, validation, and production readiness for NVIDIA, AMD, and other AI platforms. Coordinates customer and internal engineering teams to resolve hardware, firmware, PCIe/NVMe, thermal, power, performance, and interoperability issues. Identifies emerging AI storage workloads, conducts feasibility studies and proof-of-concepts, evaluates ecosystem trends, and develops future product opportunities.

Operate and improve large-scale Ethernet fabrics that support GPU clusters, storage, and management infrastructure. Monitor and resolve production incidents, execute changes and maintenance, manage hardware lifecycle, perform root-cause analysis, build telemetry and runbooks, and automate operational tasks to improve reliability and reduce toil across global AI data centers.

Lead Security Engineer designs, develops, and validates secure software solutions, performs threat modeling, vulnerability and penetration testing, and implements AI-assisted security workflows. They develop secure production code, review peers' code, work with stakeholders to mitigate risks, and apply MLOps/GenAI controls to ensure resiliency, auditability, and regulatory compliance across cloud environments.

Build and operate production-ready AI/ML systems by integrating models into applications, optimizing latency, throughput, reliability, and resource efficiency, and supporting deployment, monitoring, and troubleshooting. The role requires MLOps, cloud and DevOps familiarity, production deployment experience, and strong software engineering, machine learning, system architecture, and communication skills. Engineers will collaborate cross-functionally, lead technical projects, and help ensure enterprise security, compliance, observability, and operational readiness.

Designs and builds secure, production-grade GenAI and agentic AI applications using Python, LLMs, AWS, and Databricks. Develops scalable AI pipelines, integrates data and cloud services, applies RAG, vector databases, model evaluation, and guardrails, and creates cost-effective solutions. The engineer researches emerging tools, improves application infrastructure and performance, works independently, guides team members, and delivers under tight timelines.

Leads the technical direction, architecture, and development of scalable claims-management systems for Lyft’s insurance platform. Builds AI automation using multimodal LLMs, RAG pipelines, and agentic workflows to improve claims operations. Partners with Engineering, Data Science, Product, Claim Operations, and insurance providers; drives roadmap decisions, integrations, reliability, testing, code reviews, incident response, and engineering best practices.

Lead the architecture and development of scalable, reliable claims-management systems and AI automation solutions. Define technical roadmaps, make architectural decisions, improve claims workflows, build AI features using LLMs and RAG, collaborate with engineering, data science, product, claims operations, and insurance partners, and support code reviews, on-call operations, and incident response.

Own the architecture and operation of DAT’s shared AI and machine learning platform. Build model gateways, inference services, feature and vector storage, evaluation systems, observability, governance, and cost controls on AWS. Lead distributed-systems and cloud architecture, production incident response, engineering standards, cross-functional alignment, and mentorship. Support model training, serving, monitoring, safety, and continuous improvement across AI teams.

Develop scalable backend services and APIs while integrating Generative AI models into enterprise applications. Responsibilities include fine-tuning and optimizing LLMs, building microservices, implementing RAG pipelines, deploying applications to cloud platforms, and managing CI/CD, Docker, and Kubernetes workflows. The role also involves performance monitoring, testing, code reviews, debugging, and collaboration with engineering teams.

Design, build, and deploy autonomous AI agents and multi-step workflows for revenue cycle management, scheduling, claims processing, and clinical documentation. Implement tool-using agents with function calling, retrieval systems, memory, planning, and optimization. Collaborate with product and clinical teams, ensure HIPAA-compliant secure deployments, and continuously monitor and improve model accuracy, latency, and cost.

Design, build, and productionize ML, NLP, and generative AI (LLM/RAG) solutions for Navy maintenance and logistics data. Implement end-to-end ML pipelines, MLOps in AWS GovCloud, vector search, model governance, and documentation for Government accreditation; collaborate with data scientists, engineers, and Navy SMEs.

Design, build, and deploy LLM-powered agents and retrieval systems that autonomously execute multi-step healthcare tasks; create evaluation and monitoring infrastructure to measure and improve model quality; ship copilots and assistive features; iterate from production signals and make scalable architectural decisions.

Design, build, and optimize AI applications using LLMs, RAG, agents, and vector databases. Maintain AI infrastructure (model serving, orchestration, APIs, cloud), apply MLOps best practices, evaluate new tools, collaborate across teams, participate in architecture and code reviews, and troubleshoot for performance, scalability, reliability, and security in a cyber mission environment.

Design, develop, and maintain LLM orchestration frameworks that coordinate static analyzers, RAG systems, and automated testing. Build data preprocessing and vectorization pipelines for documentation, source code, and test results. Implement database solutions for RAG, artifact storage, and audit trails. Deploy and optimize AI tools within secure enclaves and support security assessments and ATO processes, including LLM fine-tuning for vulnerability identification and mitigation.

Build and operate infrastructure for large-scale AI model training and evaluation. Responsibilities include developing inference and grading platforms, optimizing GPU utilization and compute scheduling, diagnosing distributed-system bottlenecks, improving reliability and throughput, and creating self-service tooling, automated validation, and observability for researchers. The role requires ownership of open-ended infrastructure projects and close collaboration across research and engineering teams.

Conduct open-ended AI research focused on end-to-end training workflows using LanceDB. Design replicable experiments and benchmarks, demonstrate workflows across AI domains, develop content, publish research and models, and collaborate with engineering and product teams. The role requires deep learning expertise, experience training state-of-the-art models, open-source software development, strong prioritization, product awareness, and staying current with AI research.

Lead technical pre-sales for enterprise AI applications: run demos, PoCs, hackathons, and architecture workshops. Design secure, scalable cloud-native AI solutions, resolve technical blockers, and advise customers on AI Foundry, Azure AI, Responsible AI, and AI management/security to accelerate adoption and deployment.

Provides technical leadership for full-stack engineering and applied AI initiatives. Designs and delivers resilient microservices, event-driven systems, AI-augmented workflows, and agentic solutions using cloud, orchestration, retrieval, and model technologies. Evaluates AI use cases, establishes production monitoring and governance practices, mentors engineers, influences technology strategy, and ensures solutions meet reliability, security, performance, and operational standards.

Research and develop machine learning systems, algorithms, prototypes, and data-fusion solutions for complex sponsor problems. Responsibilities include data selection and modeling, statistical analysis, anomaly detection, visualization, software integration, technical reporting, collaboration with sponsors, and supervision of junior staff. The role supports classified defense and intelligence applications and is performed onsite in State College, Pennsylvania, or Reston, Virginia.

Builds and maintains infrastructure automation across hybrid Azure and on-premises environments. Responsibilities include Infrastructure as Code, CI/CD pipelines, backups, disaster recovery, identity and security automation, monitoring, Tier 2/3 support, and AI-enablement tooling. The role also supports manufacturing IT/OT convergence, network segmentation, industrial systems, plant infrastructure, and vendor escalations. Requires frequent collaboration with cloud, security, software, data, manufacturing, and business teams, plus bilingual Spanish and English communication.

Build and maintain infrastructure automation across hybrid Azure and on-premises environments. Responsibilities include Infrastructure as Code, CI/CD pipelines, immutable backups, disaster recovery, identity and Zero Trust automation, monitoring, self-healing workflows, AI integrations, documentation, and Tier 3 support. The role collaborates with cloud, security, software, data, and business teams while supporting SOC 2 and ISO 27001 controls.