Top AI Engineer Jobs

Leads hands-on development of enterprise AI solutions, including intelligent assistants, copilots, RAG platforms, document intelligence, and business-process automation. Designs cloud-native applications across Azure, SAP S/4HANA, and SAP BTP using Python, modern AI frameworks, Docker, Kubernetes, and CI/CD. Provides architecture guidance, mentors engineers, troubleshoots production systems, and communicates technical decisions to cross-functional stakeholders. Requires substantial software engineering experience and production Generative AI expertise.

Designs, develops, deploys, and optimizes generative AI and machine learning solutions on AWS. Builds Amazon Bedrock and RAG applications, document-processing bots, APIs, and scalable ML models. Responsibilities include model evaluation, monitoring, troubleshooting, continuous improvement, data extraction, and cross-functional AI integration. The role requires expertise in Python, cloud architecture, OCR technologies, NLP, deep learning, CI/CD, and web application development.

Supports machine learning development for fraud detection, including data preprocessing, feature engineering, model training, evaluation, experiment tracking, drift monitoring, and batch scoring. Maintains Python data pipelines, Jupyter reporting artifacts, and MLflow documentation. Assists with NLP/NER transformations and UiPath Maestro integration testing while following government security, data governance, CI/CD, and EPLC documentation requirements.

Operate and assure production AI/ML applications, foundation-model integrations, and agentic workflows. Monitor reliability, latency, errors, costs, usage, and workflow performance; investigate failures; maintain runbooks; and conduct evaluations and regression testing. Manage prompts, models, tools, retrieval sources, configurations, deployments, and rollbacks. Support cybersecurity, audit, access control, incident response, compliance evidence, and operational improvements for secure, reliable, and auditable AI services.

Designs and maintains enterprise AI/ML platform capabilities, managed model integrations, reusable APIs, agentic workflows, developer assistants, and secure automation. Integrates AI services with cloud platforms, identity controls, secrets management, observability, and enterprise tools. Develops tests, evaluates model and agent updates, troubleshoots distributed workflows, and collaborates with platform, DevSecOps, cybersecurity, and operations teams in secure and restricted environments.

Develop and test AI software and machine learning models for NASA human spaceflight applications. Responsibilities include gathering requirements, designing software, validating AI systems, troubleshooting, presenting capabilities, and following NASA software processes. The role applies AI to mission control, systems engineering, spacecraft hardware prognostics, and flight software testing while collaborating with engineers and stakeholders.

Early-career engineer responsible for characterizing AI/ML training and inference workloads, analyzing compute and memory bottlenecks, and building performance models for hardware and software systems. The role partners with architecture, compiler, runtime, and product teams on optimization decisions, benchmarking, and reproducible technical analyses. Candidates apply mathematical reasoning, computer architecture knowledge, Python-based data analysis, and machine learning frameworks. Travel is required up to 10%.

Lead technical sales engagements with AI platform, hyperscale, service provider, and data center customers. Design large-scale networking architectures, demonstrate Cisco solutions, connect technical strategy to account objectives, and drive demand generation and enablement. Advise on routing, optical systems, compute, storage, silicon, AI workloads, automation, and MLOps while building customer trust and coordinating stakeholders to deliver business outcomes.

Build full-stack internal applications, automation platforms, and AI-enabled tools for aerospace engineering and business teams. Own a major software project from requirements through deployment, developing interfaces, backend services, APIs, databases, integrations, and LLM-powered capabilities. Collaborate with stakeholders, gather user feedback, iterate on solutions, and deliver production-quality software.

Build and operate secure, scalable cloud platforms, automation, infrastructure-as-code, CI/CD pipelines, observability, and developer self-service capabilities. Support AI, machine learning, data, and generative AI workloads through deployment automation, monitoring, governance, and lifecycle tooling. Partner with engineering, security, infrastructure, and data teams to improve reliability, developer experience, compliance, and operational readiness while mentoring engineers and contributing to technical design decisions.

Develop and maintain AI-assisted automated testing frameworks across web, mobile, backend, API, data, and AI applications. Use AI tools to generate tests, analyze code, debug failures, improve coverage, and validate model outputs. Build testing strategies for LLM, RAG, agentic, and generative AI workflows, including hallucination, bias, safety, and accuracy evaluations. Integrate testing into CI/CD pipelines, establish quality gates, collaborate across engineering and product teams, and promote quality, security, observability, and testability practices.

Build Tailscale’s internal AI-enablement tools, workflows, guardrails, and documentation. Partner with engineering leadership and teams to define the AI roadmap, evaluate coding agents and models, improve adoption, and measure effects on velocity and quality. The role involves developing internal developer-facing automation, teaching and influencing engineers, identifying high-leverage AI opportunities, and promoting safe use of AI-generated code in a distributed engineering organization.

Engineer and operate AI monitoring, guardrail, observability, and governance applications in an on-premises OpenShift environment. Responsibilities include building APIs and integrations, deploying containerized services, managing model evaluation workflows, developing Grafana dashboards and alerting, troubleshooting production systems, optimizing scalability and performance, and improving controls for agentic AI applications. The role also leads cross-functional initiatives, mentors engineers, and supports operational planning in a regulated enterprise.

Own the technical sales cycle for MCP Manager, leading discovery, tailored demonstrations, proofs of concept, security reviews, and customer onboarding. Partner with Account Executives and Customer Success, troubleshoot integrations using AI coding assistants, create technical documentation and demo environments, and provide product feedback. The role requires expertise in APIs, authentication, SaaS configuration, AI agent tooling, security reviews, and customer-facing technical communication in an early-stage environment.

Design and develop AI foundations for general-purpose industrial robots, including Vision-Language-Action frameworks and Action World Models. Responsibilities include creating data-efficient learning strategies, enabling cross-robot generalization, developing real-world reinforcement learning methods, and designing human-intervention data systems. The role requires advanced knowledge of multimodal models, transformers, motor control, robotics, and machine learning, along with expert Python and PyTorch or JAX skills.

Develop, train, evaluate, debug, and improve machine learning models for real-world applications. Own the ML lifecycle from dataset construction and experimentation through deployment, while applying deep learning, reinforcement learning, and LLM techniques. Collaborate with engineering and product teams, contribute to scalable ML infrastructure and tooling, define evaluation metrics, analyze errors, and translate research concepts into practical solutions.

Own and grow the U.S. corporate sales pipeline for AI-driven software development training. Responsibilities include engaging decision-makers, identifying business needs, tailoring training solutions, selling multi-day programs and ongoing enablement, developing long-term client relationships, and contributing to pricing, packaging, and go-to-market strategy. This is a remote, commission-based, consultative B2B sales role with potential for a future base salary.

Lead the architecture, development, deployment, and reliability of production AI systems. Establish technical standards, make architecture decisions, build observability and evaluation frameworks, mentor engineers, and translate client and product needs into scalable AI solutions. The role requires strong software engineering, full-stack or backend development, system design, technical leadership, and expertise with modern AI tooling.

Build, integrate, enhance, and support production-grade Generative AI and LLM applications. Develop AI agents, RAG capabilities, APIs, and enterprise integrations using JavaScript and TypeScript. Apply prompt engineering, model evaluation, monitoring, security, authentication, and responsible AI practices. Troubleshoot complex issues across application, integration, model, and platform layers while supporting CI/CD, testing, deployments, observability, and production operations.

Lead the design and development of an enterprise agentic AI platform for wholesale credit risk model development. Partner with quantitative analysts and stakeholders to define use cases, architect secure RAG and orchestration capabilities, build integrations with data sources, APIs, and LLMs, and establish CI/CD, testing, monitoring, and governance frameworks. Lead and mentor software engineers while overseeing architecture, agile delivery, timelines, and platform milestones.

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Build and operate an AI-native web and go-to-market platform using agents, LLM pipelines, RAG, personalization, evaluation guardrails, and automated web experiences. Integrate CRM, marketing automation, analytics, enrichment, and intent systems to improve conversion and pipeline. Own performance, accessibility, SEO, AI search visibility, experimentation, and measurement while partnering across marketing, sales, product, security, and engineering.

Build foundational AI systems across model post-training, reinforcement-learning environments, distributed systems, performance optimization, backend services, APIs, SDKs, and developer tools. Own projects from definition through rollout, write reliable code, investigate complex failures, build tests and metrics, and collaborate through design reviews. The role emphasizes strong computer-science fundamentals, rapid learning, technical initiative, and ownership for early-career engineers.

Designs, builds, tests, and supports NICE Cognigy virtual agents for digital chat and omnichannel self-service. Develops conversation flows, intents, entities, NLU models, integrations, and live-agent handoffs. Monitors bot analytics and interaction data, troubleshoots production issues, maintains documentation, and ensures security, privacy, accessibility, and compliance. Collaborates with business, IT, product, and experience design teams on discovery, releases, and continuous automation improvements.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.