Top AI Engineer Jobs

Lead architecture, operation, and improvement of cloud data and analytics platforms (Fabric, Snowflake, Databricks). Own CI/CD for data assets, BI delivery (Power BI/Tableau), reliability, monitoring, incident response, security, and governance. Enable teams through standards, runbooks, and onboarding; troubleshoot performance and ensure production stability.

Design and implement LLM-powered and agentic AI workflows integrated with mission platforms. Capture user workflows, translate decision points into executable logic, streamline analytics, ensure secure production operation, and collaborate with operators, analysts, and leadership in classified environments.

Join the Enterprise AI Platforms team to build and operate agent harness capabilities that integrate agents with tools, APIs, and enterprise systems. Contribute to architecture, ensure reliability under load, advance test automation, and meet non-functional requirements (security, observability, scalability). Own assets end-to-end, document work, and collaborate with architects, product, and consuming teams.

Lead end-to-end design, build, and operation of production enterprise AI systems embedded with business teams. Rapidly prototype, deploy, and measure AI solutions using LLMs, vector DBs, and Kubernetes, then productize reusable platform capabilities, ensure observability/CI/CD/security, and mentor engineers while driving enterprise adoption and measurable business impact.

Build and maintain Geotab's internal machine learning and generative AI platform. Implement logging, monitoring, and alerting; enrich big data with telematics at scale; support data integration and infrastructure for data scientists; document architecture and processes; assist internal teams with AI platform adoption and production deployments.

Design and implement LLM‑powered and agentic workflows for intelligence missions. Capture user workflows, integrate AI with APIs and enterprise data, ensure production reliability and security in classified environments, and collaborate with operators and mission leadership to deliver AI-enabled decision support.

Design, build, and deploy AI-powered internal tools and intelligent workflows to automate People, Finance, Recruiting, and operations. Identify improvement opportunities, translate business needs into technical solutions, own end-to-end execution, and collaborate across teams to ship reliable production software and AI integrations.

Design, build, and ship AI-powered internal tools and intelligent workflows to automate operations across People, Finance, Recruiting, and other teams. Own end-to-end execution from discovery to production, integrate AI services, work cross-functionally, and iterate on deployed systems.

Design and build AI-powered internal tools and automations to improve operations across People, Finance, Recruiting, and business workflows. Own end-to-end delivery from discovery to production, integrating APIs, databases, cloud infrastructure, and AI systems while collaborating with cross-functional teams.

Lead technical pre-sales engagements for Microsoft cloud database and analytics products. Deliver PoCs, demos, hackathons, and architecture workshops; design secure, scalable solutions; resolve technical blockers; collaborate with engineering; and evangelize Fabric, Databricks, Purview and Azure database technologies to accelerate customer migrations and adoption.

Architect and build agentic AI solutions and developer-facing tooling, lead AI enablement across engineering, embed with product teams to integrate agentic workflows, ensure evaluation and observability, and produce documentation and training to drive adoption.

Embed with clients to design, build, and run production agentic AI systems. Architect retrieval, orchestration, policy routing, tool invocation, evaluation harnesses, and lifecycle observability. Integrate multiple AI providers, leverage cloud-native patterns (Kubernetes, microservices, serverless), implement CI/CD and IaC, run workshops/POCs, measure agent performance, and create reusable patterns and documentation.

Provide technical service and support for AI data center systems, including installation, maintenance, troubleshooting hardware and software, and collaborating with clients to ensure high-performance enterprise computing operations.

Seeking a Principal Data Architect to lead enterprise data platform development, collaborate with teams, and design scalable data architectures using modern technologies.

Designs, builds, and deploys production AI agents, agentic workflows, and API integrations across Microsoft and legal enterprise systems. Partners with attorneys and leadership to prioritize, govern, and scale AI solutions while enforcing security, compliance, and data governance.

Lead enterprise GenAI platform architecture and strategy; design and own agentic LLM systems at scale; drive company-wide LLM integration and cognitive automation roadmap; set engineering standards for distributed ML systems; align cross-functional and executive stakeholders; mentor and hire senior engineering talent.

Design and optimize a low-level AI inference serving stack: customize open-source frameworks, build model partitioning/scheduling, integrate with proprietary accelerators, profile and optimize across Python orchestration to C++ kernels and drivers, and enable PyTorch-native deployment tooling.

Design and implement a novel AI inference engine through hardware-software co-design. Collaborate with ML, software, and cloud engineers; model PPA trade-offs; contribute to RTL design, simulation, verification, and roadmap planning to optimize inference for modern LLM workloads.

Lead design and implementation of cloud-first, AI-integrated full-stack applications on GCP. Drive rapid prototyping and MVPs, mentor developers, define architecture, maintain CI/CD pipelines, and collaborate cross-functionally to deliver production-ready solutions.

Design, operate, and evolve ElastixAI's Kubernetes and multi-cloud inference infrastructure. Run accelerated ML workloads at scale, build deployment and automation tooling, harden AWS/GCP/on-prem systems, partner with ML/runtime teams to productionize models, optimize costs and reliability, and participate in on-call rotation.

Design and implement IR transformations, graph optimizations, kernel lowering, and code generation for novel hardware. Decompose LLM/transformer workloads into primitives, build performance models and profiling tools, collaborate with ML and hardware teams, prototype end-to-end improvements from framework passes to custom kernels, and shape system architecture for an inference engine.

The Senior Software Engineer will develop AI/ML systems for debugging in production environments, integrating predictive analytics and issue resolution tools.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

Hands-on senior role to design, develop, and support AI, analytics, data engineering, IoT, and intelligent automation solutions. Build scalable data pipelines, API integrations, streaming and operational intelligence platforms, generative AI agents, predictive maintenance models, and Power BI reporting to improve operations, asset performance, and decision-making across the enterprise.

The AI Behavior Engineer will measure and shape AI model behaviors, collaborating with stakeholders to conduct evaluations and adapt methods for various contexts.