Top AI Engineer Jobs

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Design and prototype AI-powered solutions that transform business processes. Partner with business and technology leaders to identify automation opportunities, develop agentic AI reference solutions, translate requirements, measure ROI, document processes, train users, and support adoption. The role requires hands-on expertise in LLMs, AI agents, machine learning, NLP, and data modeling, plus strong business communication and solution delivery skills.

Leads two scrum teams while remaining hands-on in software development at least 50% of the time. Designs, codes, tests, debugs, and supports secure, resilient, high-throughput distributed payment systems using Java and Spring Boot. Drives AI-assisted engineering practices, automation, CI/CD, code quality, operational stability, and responsible AI usage. Evaluates technical solutions with vendors and internal teams and leads engineering communities of practice.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and operate full-stack features for an AI-powered consulting platform. Responsibilities include frontend and backend development, data pipelines, APIs, LLM-based extraction, retrieval, chat, content generation, AI coding workflows, testing, evaluations, and quality checks. The role owns platform areas end to end, collaborates with consultants, product, and design, and translates expert judgment into reliable AI-assisted functionality.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Designs and improves security controls for enterprise AI, ML, generative AI, LLM, agent, and cloud environments. Responsibilities include AI security architecture, threat modeling, application and API security, IAM, data and model protection, monitoring, incident response, automation, and governance. The role partners with cybersecurity, IT, privacy, legal, compliance, data, application, and business teams to secure AI platforms throughout their lifecycle and address risks such as prompt injection, data leakage, excessive agency, and supply-chain compromise.

Build and ship full-stack features across frontend, backend, APIs, data, integrations, and Salesforce. Use AI-assisted development tools throughout the software development lifecycle, develop agentic and AI-powered business features, and own solutions from design through production support. Responsibilities include automated testing, CI/CD, code reviews, Agile collaboration, enterprise integrations, and maintaining secure, scalable, high-quality software.

Lead design and implementation of Copilot AI experiences and platform services across Windows, Mac, and mobile. Build secure, performant, distributed systems integrating models and agentic workflows, collaborate with cross-functional teams, ship well-tested maintainable code, and drive iterative product delivery.

Lead the expansion of Snowplow’s Firn applied AI platform, initially focused on go-to-market intelligence. Design and productionize platform services, agents, tools, MCP and LLM gateways, tracing, evaluation infrastructure, learning loops, and data integrations. Establish engineering standards for reliability, observability, cost management, documentation, and recovery while setting the technical roadmap and guiding platform architecture. Collaborate with data engineers and nontechnical internal customers, and own services as they transition into production.

Train and evaluate PyTorch computer vision models for visual localization, including feature matching, image retrieval, pose regression, and scene-coordinate regression. Build large-scale image and 3D data pipelines, run experiments on GPU infrastructure, maintain localization benchmarks, and support model export, inference optimization, deployment, and monitoring. Collaborate with research and testing teams to improve production model performance.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Build and operate production AI/ML systems at scale. Responsibilities include monitoring model performance, detecting drift, implementing evaluation loops and safety guardrails, managing fallbacks, developing automated retraining and deployment pipelines, resolving live incidents, and collaborating on cloud, on-premises, and edge architectures. The role also involves code reviews, mentoring, and promoting secure and ethical AI practices.

Design, develop, and maintain scalable full-stack AI applications across frontend, API, and backend layers. Translate business requirements into secure, reliable solutions and own the complete software lifecycle from design through support. Build AI-native systems using LLMs, retrieval-augmented generation, agentic workflows, and automation. Collaborate across teams, resolve performance and reliability issues, improve engineering standards, and develop reusable cloud-native platforms for enterprise-wide use.

Designs, deploys, and operates secure cloud and hybrid infrastructure for enterprise AI workloads. Responsibilities include container orchestration, GPU-enabled compute, APIs, microservices, CI/CD, infrastructure as code, MLOps, model serving, monitoring, data platform integration, and AI governance. The role partners with architecture, cybersecurity, data, and application teams to establish scalable AI platforms and support AI agents and models across business and manufacturing systems.

Build and maintain AI agent runtimes, enterprise integrations, connectors, APIs, and observability frameworks. Connect AI solutions securely to ERP, MES, CRM, databases, file systems, and business processes. Partner with architecture, data, infrastructure, cybersecurity, and business teams to operationalize AI use cases, establish reusable integration standards, and deliver scalable production solutions.

Builds, evaluates, secures, and supports production AI-native software. Responsibilities include developing agentic AI solutions, using coding assistants across the SDLC, evaluating models, gathering requirements, reviewing designs and code, managing changes and vendors, supporting production systems, and leading large projects. The role requires strong software engineering fundamentals and experience with AI agents, LLM/SLM applications, cloud-native development, secure APIs, and Amazon Bedrock AgentCore.

Design and build integrations connecting legacy practice management systems to an AI platform. Responsibilities include AI-assisted schema mapping, data modeling, ETL pipelines, REST/SOAP APIs, data cleansing, quality monitoring, production troubleshooting, documentation, and collaboration across architecture, product, engineering, and operations teams. The role requires applying LLM tools to schema inference, field reconciliation, anomaly detection, and data-quality automation while handling healthcare data securely.

Designs, develops, deploys, and supports enterprise automation, workflow, and AI solutions using Microsoft Power Platform, Copilot Studio, Dataverse, and Azure AI. The role integrates systems through APIs and connectors, implements copilots and intelligent document processing, establishes technical standards, leads testing and releases, and optimizes production solutions. It also provides technical leadership and guidance across automation and AI initiatives.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering judgment, reasoning quality, usefulness, correctness, and user guidance. Provide clear written feedback, distinguish response-quality levels, help define evaluation standards, and record brief video explanations. This flexible remote contract requires strong software engineering experience, familiarity with AI-assisted development workflows, and excellent English communication.

Builds and enables responsible AI solutions across the organization. Responsibilities include managing AI use-case intake, coaching teams, developing Copilot Studio agents and Power Platform automations, supporting AI-powered developer tools, establishing governance and repeatable patterns, measuring adoption, and delivering training. The role requires strong communication, software development or architecture experience, Microsoft Copilot Studio expertise, and Microsoft 365 administration experience.

Modernize enterprise IT operations through cloud engineering, platform engineering, infrastructure-as-code, automation, and AI-assisted workflows. The role designs AWS and Azure infrastructure, self-service capabilities, event-driven remediation, identity and endpoint automation, CI/CD pipelines, and safe agentic operating patterns. It also establishes engineering standards, observability, auditability, rollback controls, and metrics while troubleshooting complex cloud, networking, identity, API, and distributed-systems issues.