Top AI Engineer Jobs

Develop and deploy UiPath RPA solutions and generative AI applications using AWS Bedrock, LLMs, prompt engineering, fine-tuning, and RAG. Integrate AI with business applications and automation workflows, build secure APIs and cloud-native services, support data governance, and apply DoD responsible AI and RMF controls. The role also includes testing, documentation, architecture participation, production support, and mentoring junior developers.

Design and build large-scale, highly available Responsible AI cloud services. Lead architecture, development, release, and production readiness. Collaborate with engineers, scientists, and product managers to integrate Responsible AI features across Azure AI and related services, drive DevOps practices, and improve performance, observability, and reliability.

Build and operate secure, scalable AI and data platforms across multi-cloud environments. Develop production services, APIs, automation, AI agents, self-service capabilities, and observability. Improve platform resilience, reliability, governance, responsible AI controls, and operational efficiency while supporting teams in moving AI use cases into production. Deliver reporting on usage, cost, performance, adoption, and business value, and promote reusable engineering standards and knowledge sharing.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Develop AI data infrastructure, metadata and lineage systems, foundation models, agentic AI platforms, and knowledge-infused learning capabilities. Build and evaluate NLP, LLM, time-series, diffusion, and computer vision systems while improving reproducibility, governance, reasoning, scalability, and trustworthy AI outcomes. Partner with software teams and external collaborators to translate innovative research into production implementations.

Leads AI/ML infrastructure networking initiatives by benchmarking switches, validating ASIC and fabric features, and guiding workload deployments. Develops AI-driven network automation using Python, Ansible, SONiC, Junos, and related tools. Tests protocols, congestion behavior, traffic distribution, resiliency, and high-performance network operations. Documents architectures, methodologies, configurations, results, and recommendations for scalable AI/ML network fabrics.

Design and implement compiler transformations, distributed runtimes, pipeline parallelism, sharding, and communication-computation overlap for large-scale deep learning training on GPU clusters. Develop JAX and XLA integrations, optimize compiler and runtime performance, support open-source JaxPP, collaborate with framework and hardware teams, and debug large training workloads.

Designs, builds, debugs, and optimizes scalable AI systems, including inference infrastructure, APIs, distributed services, and platform components. The role delivers secure, reliable, high-performance AI experiences across audio, image, video, and safety applications. Responsibilities include full-stack development, cross-team collaboration, architectural innovation, rapid issue resolution, and end-to-end feature delivery for systems serving millions of customers.

Build and ship AI-native workflows and prototypes for finance/accounting use cases, process large unstructured datasets, iterate with customer feedback, and design analytics-driven evaluation systems to improve product stability and value.

Operate and improve a high-throughput AI security platform running on AWS EKS. Responsibilities include Kubernetes operations, troubleshooting, upgrades, Terraform and Helm infrastructure management, autoscaling, performance testing, observability, SLOs, incident response, and implementation of cloud security controls such as IAM, secrets management, encryption, network controls, image hardening, patching, ATO evidence, and continuous monitoring.

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Design, deploy, and troubleshoot AI automation solutions across interconnected systems. Develop reusable prompts, workflows, copilots, intelligent process automations, and RAG pipelines. Integrate APIs, databases, and enterprise platforms while improving automation coverage, efficiency, and AI performance. Collaborate with stakeholders to define use cases and metrics, optimize token usage and LLM costs, and participate in responsible AI governance and risk reviews.

Lead company-wide AI transformation by designing unified distributed AI platforms, integrating frontier LLM ecosystems, and embedding with autonomous driving engineering teams. Build scalable LLMOps and MLOps pipelines, optimize GPU infrastructure and inference performance, develop AI-native engineering tools, and establish technical roadmaps. The role combines hands-on systems engineering, enterprise AI platform development, cross-functional influence, and strategic leadership across perception, prediction, planning, control, and simulation teams.

Develop scalable AI-powered operations and ML infrastructure for reliable, secure cloud systems. Design distributed systems, write maintainable code, improve architecture and operational excellence, mentor engineers, establish engineering standards, and lead testing, incident response, and on-call activities. The role requires Azure or cloud experience, coding proficiency, and expertise in automation, reliability, CI/CD, infrastructure as code, and responsible AI practices.

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.

Designs and implements compiler, runtime, and system software for Microsoft AI accelerator devices. Responsibilities include transforming code through LLVM IR, developing JIT compilation and dynamic code-generation features, deploying application code, and collaborating with hardware architects and ML developers. The role requires hands-on C/C++ and Python development, optimized systems-level programming, Unix-like platforms, and compiler framework experience.

Provide principal-level technical leadership for Microsoft's Perception AI platform. Define architectures for generative AI, RAG, memory, reasoning, agentic systems, identity, authorization, safety, reliability, and distributed cloud services. Establish reusable platform patterns, security guardrails, evaluation and observability frameworks, and cross-organization technical strategies. Partner with engineering, research, product, security, privacy, and compliance teams while mentoring senior technical leaders and driving scalable, secure, compliant enterprise AI solutions.

Develop AI models and algorithms for biomolecular modeling and simulation. Responsibilities include integrating large-scale molecular and laboratory data, writing research code, orchestrating data generation and training campaigns, developing deep learning architectures and generative models, publishing research, and collaborating with an interdisciplinary international team.

Develop and operate production AI framework components, runtimes, benchmarking systems, performance tools, and integrations. Benchmark and optimize large language model training and inference across GPUs and Microsoft hardware, build automation and observability, investigate cross-stack performance and reliability issues, and deliver scalable platform improvements. Principal engineers additionally set technical direction, lead multi-team initiatives, define architectures, and mentor technical leaders.

Interns will collaborate on end-to-end AI platform, enterprise AI services, and data engineering projects. Responsibilities may include building data pipelines, APIs, microservices, AI services, agents, workflows, automated tests, cloud deployments, and documentation. Interns will apply governance, security, privacy, observability, and data quality practices while troubleshooting issues, improving reliability, and presenting outcomes to technical and business stakeholders.

Own and optimize deep learning training performance across GPUs, cloud infrastructure, and distributed environments. Profile workloads, develop custom GPU kernels, tune PyTorch and training systems, improve memory and compute efficiency, and design scalable distributed training strategies. Collaborate with researchers and infrastructure teams to preserve numerical correctness while improving speed, cost, reliability, and scalability. Build benchmarking, checkpointing, recovery, and regression-testing workflows for long-running AI/ML experiments.

Designs and develops full-stack web applications, data products, APIs, and reusable platform capabilities. Builds React interfaces and Python backend services, integrates analytics and AI capabilities, and manages CI/CD, containerization, testing, deployment, monitoring, and data quality. Collaborates with data engineering, analytics, product, UX, and business teams to deliver scalable solutions using technologies such as Databricks and Azure.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Lead end-to-end data engineering workstreams, building reliable cloud pipelines, governed AI and retrieval workflows, trusted data models, event-driven systems, and automated recovery. Own production reliability, security, observability, cost optimization, testing, audit evidence, and operational readiness. Use Python, advanced SQL, coding agents, machine learning, and defensive AI engineering to deliver secure, resilient platforms while mentoring engineers and translating business needs into practical solutions.

Advises on enterprise AI security across the model and agent lifecycle. Responsibilities include AI-BOM and AI-SPM governance, pre- and post-production model scanning, CI/CD security gates, AI asset inventory, SaaS agent security, Shadow AI discovery, threat modeling, remediation tracking, application-level troubleshooting, and documentation of security policies, standards, and runbooks.