Top AI Engineer Jobs

Build and operate production machine learning systems enabling autonomous medical coding at scale. Responsibilities include data discovery, feature engineering, model training and evaluation, deployment, monitoring, MLOps pipelines, LLM integration, observability, governance, fairness assessment, and A/B testing. The role also involves designing highly available low-latency architectures, defining technical roadmaps, and collaborating with product, design, data, and platform teams.

Leads enterprise AI application architecture and hands-on development for LLM, agentic, multi-agent, and RAG solutions. Establishes scalable design patterns, governance standards, observability, reliability, and compliance practices. Collaborates with product, engineering, and compliance teams, evaluates emerging AI technologies, optimizes performance, and mentors senior engineers. The role requires extensive software engineering and enterprise AI architecture experience, with expertise in cloud-native systems, Kubernetes, CI/CD, MLOps, and production AI platforms.

Designs and builds AWS data ingestion pipelines for structured and unstructured data supporting analytics and AI/GenAI use cases. Responsibilities include developing data lake ingestion frameworks, preparing AI-ready datasets through document parsing, chunking, and embedding pipelines, and creating reusable metadata-driven solutions. Requires AWS services, Python, and PySpark experience, with Bedrock, vector databases, and OpenSearch as preferred skills.

Owns architecture, technical standards, and production delivery for Infrastructure AI Ops solutions, including telemetry pipelines, topology graphs, infrastructure-as-code remediation, anomaly detection, capacity modeling, and autonomous self-healing automation. Serves as a critical-incident escalation point, makes automation safety decisions, develops governance evidence for regulated environments, mentors senior engineers, and remains hands-on with production code and infrastructure.

Leads architecture, development, and operation of production AIOps systems. Establishes reusable ML, evaluation, safety, and remediation-automation standards; advances use cases toward guarded or self-healing autonomy; manages major incident escalations and kill-switch decisions; leads postmortems; mentors senior engineers; and represents engineering to risk, audit, and governance stakeholders.

Build and operate AI Ops systems for application operations in a regulated financial environment. Responsibilities include developing telemetry ingestion and feature pipelines, training anomaly detection and forecasting models, building LLM-based log analysis, correlating events, and creating tested remediation automation with rollback and safety controls. The role also requires production model monitoring, retraining, incident response, and participation in an on-call rotation.

Conduct applied research and experimentation on AI, machine learning, generative AI, AIOps, observability, and automation for application and infrastructure operations. Develop prototypes, evaluation frameworks, and experiments; assess accuracy, reliability, scalability, risks, and production readiness; and translate validated findings into technical recommendations and engineering opportunities. Communicate results to technical and business stakeholders while considering governance, privacy, security, and operational risk.

Build and operate AI-enabled infrastructure operations, including telemetry ingestion and feature pipelines, anomaly detection and forecasting models, topology graphs, event correlation, and guarded remediation automation. Develop rollback-enabled Terraform or Ansible workflows for infrastructure changes, monitor models and automations in production, support retraining and drift detection, and participate in on-call operations within a regulated financial environment.

Design, build, deploy, and support production AI applications for complex enterprise workflows. Develop LLM, retrieval, agentic, API, and distributed-service capabilities; create evaluation, testing, tracing, and observability practices; investigate AI failures; and optimize quality, security, performance, maintainability, and cost. Collaborate with domain experts, architects, data teams, and software engineers to translate specialized knowledge into reliable, explainable, and maintainable solutions.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, workflow automations, and agentic solutions. Translate requirements into software components, scripts, integrations, and configurations while learning SDLC, security, privacy, and responsible AI practices. Support prompt engineering, retrieval-assisted use cases, API integrations, unit testing, troubleshooting, code reviews, demonstrations, and documentation. Collaborate with technical and non-technical teams and use approved AI-assisted development tools under senior guidance.

Develop and deploy AI capabilities for national security customers using Claude. Advise on architecture, build prototypes and evaluation frameworks, lead technical workshops and code reviews, and guide secure implementations. Work closely with customer engineering and mission teams, often on-site in classified environments. Translate mission requirements into technical solutions, support LLM applications, and share customer insights with Anthropic’s Product and Engineering teams.

Leads architecture and hands-on implementation of scalable AI Defense systems that detect, validate, and stop threats to AI applications, agents, and foundation models. Owns high-performance platform services, LLM inference and model-hosting strategy, secure enterprise deployments, and production delivery. Partners with Product, Threat Research, and Operations while mentoring engineers and guiding technical strategy across teams.

Build and operate production AI infrastructure for multi-agent systems across customer clouds. Develop Python or Go tooling, CLIs, controllers, and services; deploy and optimize LLM inference with Kubernetes and vLLM; manage MLOps, model releases, observability, CI/CD, GitOps, security, and multi-cloud platform reliability. The role also involves Kubernetes operators, Terraform, GPU scheduling, agent runtimes, evaluation pipelines, and developer self-service automation.

Lead full-stack development of agentic AI workflows and a payroll platform using React, TypeScript, .NET, event-driven systems, and Azure. Design secure, scalable architectures; supervise AI-generated code; establish prompts, evaluations, guardrails, and testing practices; deliver frontend and backend features; own automated unit, integration, and end-to-end testing; and mentor engineers while influencing technical direction and business outcomes.

Architect and build Verizon’s enterprise AI control plane, integrating AI developer platforms with governance hubs. Develop APIs, CI/CD governance pipelines, responsible AI and security guardrails, agent simulation, observability telemetry, IAM, and secure routing for autonomous workflows. Collaborate with platform engineering, DevOps, DataOps, data science, and AI product teams to operationalize LLMs and agentic systems at enterprise scale.

Develop AI-powered cyber defense applications, full-stack interfaces, backend services, APIs, agent workflows, and secure integrations. Connect security telemetry, machine learning models, inference services, and response tools while implementing controlled autonomy, access controls, audit trails, testing sandboxes, and monitoring. Evaluate agent behavior and support production infrastructure through troubleshooting, maintenance, documentation, and collaboration with cybersecurity and infrastructure specialists.

Design and deploy generative AI and machine learning solutions for customers, spanning use-case scoping, data exploration, model development, evaluation, and deployment. Build RAG systems, fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Lead customer relationships, stakeholder education, workshops, and strategic initiatives while translating recurring challenges into reusable tooling, workflows, and best practices. Collaborate with Solutions, Product, and Applied AI teams to guide roadmaps and deliver business value.

Design and implement real-time localization, ego-motion tracking, and state-estimation systems by fusing multi-modal sensors. Build scalable offline pose-tracking pipelines, multi-sensor calibration tools, and evaluation/testing frameworks. Collaborate cross-functionally to deploy reliable, high-performance software for humanoid robotics in real-world environments.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, and agentic workflow automations. Translate requirements into software components, scripts, integrations, and configurations while learning software development, security, privacy, and responsible AI practices. Support prompt engineering, retrieval-assisted use cases, API integrations, testing, troubleshooting, documentation, code reviews, and team collaboration. The role is designed for early-career candidates with coursework, projects, internships, research, open-source work, or portfolios demonstrating programming, AI, automation, data, or problem-solving skills.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, and agentic workflow automations. Translate requirements into software components, scripts, integrations, and configurations; support prompt engineering, retrieval-assisted use cases, and API integrations. Perform testing and troubleshooting, participate in code reviews and demos, and follow security, privacy, compliance, and responsible AI practices while developing software engineering skills under senior-team guidance.

Designs and owns production-grade AI infrastructure across Azure, Oracle Cloud, GCP, and AWS. Responsibilities include Kubernetes and Terraform deployments, portable multi-cloud infrastructure, CI/CD and GitOps systems, observability, security, reliability, and automation frameworks. The role builds self-service tooling, automates provisioning and operational runbooks, integrates compliance checks, and partners with product and platform engineering teams. It requires deep cloud infrastructure, networking, production operations, and DevOps expertise.

Build and maintain AI-accelerated automated testing across web, API, and mobile using Playwright. Integrate quality gates into GitHub Actions, expand functional, regression, API, and performance coverage, reduce flaky tests, and perform exploratory testing. Evaluate AI-generated tests and non-deterministic AI features, track quality metrics, convert escaped defects into regression tests, and collaborate with engineering teams. Mentor QA engineers and improve testing processes across the organization.

Design, train, evaluate, and optimize machine learning models for wireless network use cases and real-time edge inference. Build telemetry data pipelines, deploy containerized models on Kubernetes and GPU infrastructure, develop AI-driven network applications, and implement MLOps practices. Benchmark resource contention, ensure model security and provenance, document results, and collaborate with wireless, RF, cybersecurity, and government stakeholders. The role requires hands-on lab work, technical communication, and eligibility for a Secret clearance.

Design and implement multi-agent AI systems for marketing compliance workflows. Build production ML pipelines involving model fine-tuning, prompt engineering, retrieval-augmented generation, evaluation, monitoring, explainability, and feedback-driven personalization. Translate compliance rules into agent logic, ensure auditability across customer deployments, and collaborate with engineering, product, implementation, and compliance teams to deliver reliable AI features.

Leads the design, development, and deployment of AI-driven CAD automation and engineering applications. Builds NX and Creo customizations, integrates CAD with PLM systems, develops data pipelines, and applies machine learning, generative AI, and optimization to design, validation, routing, configuration, and change-management workflows. Serves as an AI/CAD subject matter expert, establishes technical roadmaps, mentors engineers, and drives production adoption of intelligent engineering tools.