Top AI Engineer Jobs

Designs and leads globally distributed AI infrastructure that routes, prioritizes, and allocates computing capacity for Azure OpenAI and Microsoft Copilot. Responsibilities include coding, architecture, capacity optimization, reliability engineering, incident response, secure deployments, cross-team delivery, mentoring, and operating highly available services. The role also advances AI-assisted software development and responsible AI practices.

Build and scale Surgo’s Python-based AI interview engine, including survey execution, LLM interviewer behavior, APIs, data models, integrations, and Azure infrastructure. Own PostgreSQL migrations, reliability, observability, testing, CI/CD, security, privacy, and compliance for sensitive participant data. Collaborate with product, research, frontend, and data teams while delivering features independently in a remote, cross-functional environment.

Build and own full-stack AI applications, data APIs, dashboards, and cloud-native services. Develop Python and Node.js backends, React frontends, AI/ML integrations, RAG pipelines, authentication, API management, testing, CI/CD, and performance improvements. Collaborate with AI/ML engineers, data scientists, product, and design teams to deliver scalable, reliable, intuitive, and responsible AI experiences.

Build and evolve ngrok’s AI Gateway, including real-time AI traffic handling, policy enforcement, rate limits, usage caps, access rules, token tracking, observability, and explainability. Design customer-focused gateway abstractions and collaborate with Gateway, Customer Data, Platform, product, and design teams to align enforcement, usage, and billing signals. The role involves full-stack development using primarily Go and TypeScript in a production AWS, Kubernetes, and distributed-systems environment.

Leads the technical architecture and development of liquid cooling systems for AI data centers and high-density computing. Designs direct-to-chip, immersion, and two-phase cooling systems; develops system roadmaps; evaluates pumps, valves, heat exchangers, piping, and controls; translates customer needs into technical specifications; collaborates with NVIDIA and other partners; mentors engineers; and represents the company in ASHRAE and Open Compute Project standards organizations.

Lead AI-driven software engineering and enterprise platform modernization. Build full-stack applications using TypeScript, Node.js, NestJS, React, and Python; integrate LLMs, generative AI, workflow automation, and intelligent decisioning; modernize legacy systems into cloud-native architectures; and establish AI-assisted development, governance, testing, security, and engineering best practices across AWS, Azure, and GCP.

Build and deploy AI-powered tools, workflow automations, integrations, prototypes, and business solutions for Wiley Research. Translate ambiguous operational problems into technical requirements, develop LLM and automation solutions, integrate Microsoft 365 and enterprise systems, and collaborate with nontechnical stakeholders. The role also evaluates AI platforms, supports sprint delivery, ensures security and governance compliance, documents architectures, and improves development practices.

Own the architecture and production reliability of Ande’s AI agent system. Build agent tools, safety controls, evaluation suites, venue correspondence workflows, memory, supply-learning systems, and typed classifiers. Improve latency, failover, quality, and safe handling of real-money actions while mentoring engineers and defining AI-native development practices. The role requires strong TypeScript and backend systems expertise, production LLM agent experience, and judgment about deterministic versus model-driven behavior.

Builds and operates reliable, governed data pipelines and AI platform services on Databricks and Azure. Responsibilities include batch and streaming ingestion, transformation, orchestration, feature and retrieval pipelines, evaluation data, model and agent monitoring, MLOps and LLMOps infrastructure, CI/CD, infrastructure as code, observability, incident response, and cost optimization. The role also mentors engineers, supports AI agent implementations, maintains semantic layers, and partners with architecture and analytics teams to deliver dependable data products.

Serves as the on-site engineer for installing, administering, securing, and authorizing EdgeRunner AI software across DoD networks. Coordinates RMF, STIG, ACAS, SCAP, and eMASS activities; manages hosts, accounts, patches, backups, and compliance evidence. Works with JOC personnel to identify automation opportunities, build AI agents and simple web views, prepare operational data, train users, and support system handoff. Classified-network support may include SIPR and JWICS installations.

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

Leads engineering teams and portfolios, architects scalable cloud-native systems, and develops full-stack distributed applications. Designs AI- and ML-enabled solutions using LLMs and modern cloud infrastructure, mentors engineers, drives architectural standards, and partners with product leaders. Responsibilities include building reliable microservices, APIs, data platforms, and personalized customer experiences using Java, JavaScript, TypeScript, Python, Go, AWS, databases, containers, CI/CD, observability, and automated testing.

Leads architecture, deployment, monitoring, automation, governance, and lifecycle management for production machine learning systems. Manages datasets, ML pipelines, model registries, evaluation, drift monitoring, testing, rollback, security, compliance, and audit documentation. Partners with data, cloud, software, cybersecurity, testing, operations, and government stakeholders to deliver secure, traceable, mission-critical ML capabilities.

Build and operate production machine learning capabilities, including dataset management, automated training and deployment pipelines, model monitoring, drift detection, governance, testing, rollback, and secure cloud integrations. Collaborate with data, software, cloud, cybersecurity, testing, operations, and government teams. Maintain traceability, documentation, auditability, incident response, and human-review controls across the model lifecycle.

Designs and secures AI and cloud solutions for government operations, including knowledge assistants, retrieval workflows, bounded agents, APIs, integrations, authentication, authorization, audit logging, testing, monitoring, and human-approval controls. Translates mission requirements into scalable architectures and supports documentation, security compliance, troubleshooting, deployment, and technical guidance. Requires active Secret clearance, a relevant bachelor’s degree, and at least 10 years of experience.

Build AI and machine learning systems for analog and mixed-signal IC design, including structured artifact modeling, design-space optimization, physics-informed surrogate models, scalable data and compute infrastructure, and agentic engineering tools. The role requires deep expertise in machine learning, large-scale systems, applied mathematics, optimization, or circuit design, plus strong Python software development skills and the ability to translate research into reliable, reproducible systems.

Designs and executes functional, regression, integration, automated, voice, and end-to-end testing for conversational AI, IVR, and customer engagement platforms. Validates intents, entities, utterances, speech recognition, call routing, integrations, APIs, and customer journeys. Creates test plans, cases, automation suites, reports, and quality metrics; tracks defects and collaborates with technical and product teams. Requires extensive Cyara experience, conversational AI platform testing, IVR validation, and strong QA methodologies.

Design and validate mixed-signal hardware, power electronics, and high-speed digital systems. Perform circuit, sensitivity, and signal-integrity analysis; support PCB design, board bring-up, and testing. Develop embedded firmware for control and power regulation systems, while integrating AI tools to automate engineering workflows and analysis. Serve as a technical liaison between IT infrastructure and design engineering teams.

Lead and execute high-impact AI initiatives, serving as a technical authority on agentic systems and fullstack development, while mentoring and optimizing infrastructure strategy.

Own end-to-end dealership implementations for AI products, including discovery, configuration, data validation, go-live, hypercare, troubleshooting, training, and change management. Manage project timelines and stakeholders, diagnose logs and integration issues, escalate product bugs, create runbooks and SOPs, and relay field feedback to product and engineering teams. The role is customer-facing, hybrid, and requires 50–70% travel to client dealerships.

Support early-career AI/ML development for Intelligence Community missions by prototyping GenAI, LLM, NLP, and computer vision solutions. Responsibilities include configuring development environments, containerizing applications with Docker, integrating prototypes into CI/CD pipelines, deploying to AWS or Azure secure environments, preparing customer demonstrations, and documenting APIs and systems. The role requires hybrid work in Northern Virginia, including classified SCIF and customer facilities.

Design and build the production foundation for WGU’s AI student assistant, including retrieval, inference, conversation state, security, privacy, guardrails, evaluation, observability, and service integration. Develop full-stack features in Java and Angular, integrate enterprise systems, support production operations, and mentor engineers. The role requires hands-on LLM application development, authorization-aware data access, and technical leadership across security, reliability, scalability, and cost.

Develop and productionize large language models and agentic AI systems at scale. Design APIs, libraries, container strategies, and LLMOps infrastructure; optimize deep learning training and inference on specialized hardware; and lead AI projects from research and data exploration through implementation and productization. Collaborate with research scientists, engineers, and data scientists across multimodal AI, NLP, computer vision, knowledge representation, and generative AI.

Designs, develops, deploys, and operates intelligent automation solutions using AI/ML, open-source LLMs, and MCP integrations. Builds document-processing and data-cleansing bots, agentic workflows, and reusable automation components. Owns MLOps pipelines, model retraining, governance, token optimization, monitoring, troubleshooting, and performance tuning. Partners with stakeholders to identify automation opportunities, ensures security and compliance, and evaluates emerging models and frameworks.

Designs, develops, deploys, and supports full-stack intelligent applications integrating AI/ML models with business workflows. Builds reusable document processing, data extraction, classification, and data cleansing solutions; develops MCP servers and tools; manages model deployment, monitoring, retraining, and optimization. Troubleshoots production issues, performs root cause analysis, maintains operational documentation, and collaborates with business, data, infrastructure, security, and platform teams.