Top AI Engineer Jobs

Designs, develops, deploys, and supports scalable AI applications, generative AI and agentic solutions, automation, and digital agents. The role covers the full AI lifecycle, enterprise AI platform operations, cloud-native engineering, monitoring, security, reliability, and continuous improvement. It leads technical workstreams, collaborates with business and technology teams, and mentors less experienced engineers.

Build and ship production LLM-powered coding tools, internal AI products, multi-agent systems, and agentic workflows. Develop tool-calling, API orchestration, structured output, evaluation, benchmarking, and scalable Python backend systems. Evaluate models based on performance and cost, collaborate across product, security, and engineering teams, and independently deliver AI systems end to end.

Develop AI agents and secure MCP servers for IAM and enterprise applications. Build Python and Node.js backend services, REST APIs, and modern React or Vue.js interfaces. Integrate LLMs and generative AI into existing workflows while applying prompt engineering, tool integration, security, scalability, and performance best practices. Collaborate with engineering and product teams to deliver AI-powered enterprise solutions.

Design, build, deploy, monitor, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and tooling.

Design, prototype, deploy, and integrate generative and predictive AI solutions for U.S. government customers. Develop agentic workflows, fine-tune LLMs, build customer-facing interfaces, research emerging AI capabilities, and translate technical concepts for customers and subject matter experts. Support pre-sales efforts and create both rapid proofs of concept and scalable long-term implementations.

Design, build, and deploy autonomous AI agents by developing agent architectures, decision logic, memory systems, tool integration, inference flows, evaluation, and monitoring. Deliver scalable, production-ready agentic systems and collaborate with engineering teams to ensure reliable, safe, and observable multi-step agent behavior across distributed environments.

Provide technical advisory and hands-on AI engineering support for design, evaluation, and advancement of AI-enabled applications. Assess LLMs, RAG, agentic workflows, orchestration frameworks, and architectures; develop prototypes, guidance, and technical recommendations; support test and evaluation activities and brief government stakeholders while ensuring operational relevance.

Designs, secures, deploys, and maintains cloud-native AI platforms and DevSecOps infrastructure in AWS GovCloud and regulated environments. Builds CI/CD pipelines, infrastructure-as-code, automated testing, AI/ML workflows, integrations, monitoring, and security hardening. Supports ATO readiness, compliance, prototype-to-production transitions, product commercialization, technical strategy, demonstrations, proposals, and customer engagements.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, scalability, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering talent.

Develop and deploy foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish technical standards and governance, lead design reviews, contribute to long-term AI roadmaps, and mentor senior AI engineering staff.

Design, develop, deploy, and support foundational AI systems, including large language model inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Owns the build, deployment, reliability, and operational tooling for ServiceNow Otto across SaaS environments. Responsibilities include Kubernetes infrastructure, CI/CD, GitOps, infrastructure as code, observability, incident response, cloud architecture, and production operations for AI-native services. The role partners with software engineers and applied scientists to improve deployment velocity, reliability, scalability, security, and cost efficiency while operating LLM-powered and agentic applications.

Lead the DevOps practice for ServiceNow Otto’s conversational AI platform. Own cloud-native build, deployment, release automation, Kubernetes infrastructure, observability, incident response, and reliability across SaaS environments. Develop infrastructure tooling, CI/CD and GitOps workflows, and secure, scalable infrastructure automation. Partner with scientists and engineers to operate LLM and agentic services in production while improving deployment velocity, resilience, cost efficiency, and operational performance.

The AI Application Engineer will design and deploy AI solutions, integrate them into cloud services, and collaborate with teams to enhance operational workflows.

Build and operate production-grade agentic AI and large language model services for Finance workflows. Responsibilities include developing AWS cloud-native platforms, retrieval and context-engineering capabilities, APIs, front-end integrations, evaluation and observability systems, and scalable event-driven services. The role also involves optimizing performance and cost, implementing secure AI guardrails, troubleshooting distributed systems, and contributing to technical standards, documentation, code reviews, and responsible AI development practices.

Own Oracle Fusion Cloud security architecture, environment administration, role governance, and platform integrity. Manage environment refreshes, cloning, quarterly updates, security roles, least-privilege controls, segregation of duties, and Risk Management Cloud controls. Provide advanced troubleshooting and root-cause analysis while designing AI, MCP, Terraform, and automation solutions to improve security operations and governance across Oracle Cloud.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Leads the design, development, deployment, and operation of enterprise AI and machine learning solutions, including generative AI, LLMs, conversational AI, predictive analytics, RAG, and agentic systems. Provides technical direction, mentors engineers, establishes responsible AI governance and model lifecycle practices, evaluates cloud AI services, and aligns initiatives with enterprise strategy. Ensures production quality, operational excellence, regulatory compliance, and delivery of healthcare-focused AI platforms.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Owns the enterprise AI engineering foundation, including shared AI platforms, semantic routing, model hosting, gateways, operations, agentic pipelines, and multi-agent orchestration. The role establishes architecture and standards, delivers secure production AI services, develops evaluation and governance capabilities, manages AI economics, prototypes emerging technologies, mentors senior engineers, and partners with security, risk, compliance, and data governance teams in a regulated financial and healthcare benefits environment.

Build and operate security services and guardrails for AI and machine learning workloads on AWS. Develop production Python code, design cloud security controls, manage Terraform infrastructure, lead technical designs and vendor evaluations, and ensure reliable, scalable implementations. The role also addresses generative AI misuse through input validation, abuse detection, identity and access controls, encryption, logging, monitoring, and policy enforcement.

Own the architecture, deployment, and reliability of CreateOS’s agentic AI platform. Build orchestration, RAG, memory, routing, guardrails, evaluation, and observability systems; integrate LLMs into production features; and ship full-stack AI applications. Partner with business stakeholders, define technical requirements, maintain production engineering standards, guide architecture, mentor engineers, and establish scalable development patterns for AI systems serving users at scale.

Design, develop, modify, diagnose, prototype, and deploy machine learning, deep learning, image processing, and computer vision algorithms. Train and deploy production ML/AI models, create model architectures, modify loss functions, and contribute independently and collaboratively to semiconductor inspection technology. Strong communication and problem-solving skills are required.

Build and productionize AI agent capabilities for revenue workflows. Responsibilities include designing agent harnesses, execution loops, tool interfaces, context and memory systems; shipping end-to-end AI features; creating evaluations and analyzing execution traces; improving reliability, latency, cost, and error recovery; experimenting with models, prompting, retrieval, and orchestration; and developing reusable AI engineering foundations.