Top AI Engineer Jobs

Design, build, deploy, and operate production AI agents and multi-agent systems for insurance workflows. Responsibilities include agent orchestration, APIs, RAG pipelines, evaluation frameworks, regression testing, observability, production troubleshooting, and reliability improvements. The role requires hands-on Python software engineering, LLM application development, cloud infrastructure, and collaboration in end-to-end product pods.

Build and operate foundational AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Lead architecture, optimization, reliability, and deployment of scalable AI platforms; improve GPU/TPU utilization, latency, throughput, and cost. Partner across engineering, research, product, and infrastructure teams, guide technical strategy, conduct deployment reviews, and mentor senior technical staff.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead orchestration, cost-performance governance, technical standards, design reviews, and roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and cost efficiency while mentoring senior technical staff and ensuring responsible AI practices.

Build and deploy scalable generative AI platform services, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor senior technical staff.

Leads the AI-first engineering vision and roadmap for an enterprise platform. Drives AI-enabled SDLC adoption, generative and agentic AI implementation, intelligent automation, governance, responsible AI controls, model evaluation, security guardrails, and DevSecOps automation. Mentors engineering teams on prompt engineering, RAG, agent frameworks, observability, and AI architecture while serving as a senior technical escalation point for cloud, data, application, and production issues.

Lead the design, development, and adoption of production-grade AI systems within a strategic business domain. Define AI roadmaps, identify high-impact opportunities, make architecture decisions, establish evaluation and observability practices, measure business impact, mentor engineers, and drive AI transformation across cross-functional teams. The role focuses on LLMs, RAG, AI agents, scalable infrastructure, and reliable enterprise AI products.

Build and operate production-grade full-stack AI applications and custom agents using MCP, RAG, LLMs, and cloud-native technologies. Own features throughout design, implementation, testing, deployment, observability, and lifecycle support. Integrate AI services, APIs, databases, and Kubernetes platforms while ensuring reliability, security, scalability, and maintainability for critical manufacturing systems. Collaborate across engineering and business teams and use AI-assisted development practices to accelerate delivery.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Leads architecture and development of frontend manufacturing applications while establishing organization-wide standards for AI-assisted software engineering. Responsibilities include coding across .NET, C++, Angular, Perl, and Python; modernizing legacy systems; resolving production issues; evaluating AI tooling; governing AI-generated code quality and security; measuring productivity outcomes; mentoring senior engineers; and influencing technical direction across multiple teams.

Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.

Design and ship production LLM features for a SaaS product: build customer intelligence from recordings and text, prioritize testimonials using metadata, create AI-powered surveys/interviews, iterate via research, prototyping, deployment, and evaluation. Own technical decisions and deliver measurable outcomes working closely with engineering leadership and customers.

Develop AI agents, copilots, full-stack applications, backend services, and enterprise integrations for internal business workflows. Build React and TypeScript interfaces, automate data processing, integrate LLM APIs, write tests, troubleshoot defects, and support deployments. Collaborate with engineers and business stakeholders across Finance, Supply Chain, HR, Legal, and data center operations while following security, monitoring, documentation, and code-quality practices.

AI Engineer Interns will research, design, evaluate, and productionize AI-powered data capabilities. Responsibilities include running AI/ML experiments, developing agent self-improvement and continuous-learning systems, collaborating with product engineers, building evaluation and training infrastructure, optimizing performance, designing APIs, and contributing across the full development lifecycle.

Design, build, deploy, and operate production-grade AI agents, automations, and internal tools. Lead workflow discovery, multi-agent orchestration, context and state management, integrations, full-stack interfaces, evaluation, guardrails, testing, monitoring, and staged releases. Measure business outcomes and determine when agents, simpler automation, or no solution is appropriate. The role requires ownership of agent systems from architecture through production improvement and reuse.

Build and own production agent systems, including orchestration loops, tool use, retrieval-augmented generation, evaluation gates, and MCP servers. Design reliable control flow with retries, timeouts, state management, grounding, and failure recovery. Develop regression corpora and judge-based gates to determine whether agent-generated code and content can ship safely. Maintain APIs under malformed or hostile inputs and evolving system rules while delivering both architecture and deployed code.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI cost-performance governance, lead technical design reviews, shape foundational AI roadmaps, and mentor senior AI engineering leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Secure Realtor.com’s AI platforms across AWS and GCP by designing agent guardrails, identity controls, containment, supply-chain protections, detection, and incident response. Lead threat modeling, secure CI/CD, AI red teaming, and secure-by-default platform frameworks. Partner with enterprise cybersecurity, build security automation using AI agents, educate teams, evaluate emerging threats and vendors, and advise leadership on AI governance and risk.

Lead the end-to-end development of an AI-powered operating system for a diversified business group. Build data ingestion pipelines, shared data models, reconciliation engines, function-specific AI agents, vehicle-routing optimization, forecasting, document AI, evaluation systems, and secure cloud infrastructure. Work directly with the owner, embed with operational teams, translate business problems into technical plans, deploy production systems, and measure business outcomes as the company transitions to AI-native operations.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Designs, builds, deploys, and scales agentic AI solutions for healthcare clients. Responsibilities include architecting systems integrating LLMs, machine learning models, APIs, and enterprise platforms; developing production-grade AI applications; evaluating agent performance; troubleshooting cloud-based distributed systems; and translating business and clinical needs into actionable technical solutions. The role also leads client engagements, collaborates cross-functionally, and communicates recommendations to technical and executive stakeholders.

Designs and deploys production-grade full-stack AI applications for autonomous semiconductor manufacturing. Responsibilities span data pipelines, model training and inference, APIs, web interfaces, digital twins, robotics integrations, LLMs, cloud and edge deployments, container orchestration, GPU optimization, monitoring, and Responsible AI practices.

Architect and engineer secure, scalable enterprise AI infrastructure for LLM training and inference, machine learning, agentic AI, and production platforms. Lead HPC, GPU, Kubernetes, cloud, networking, storage, reliability, observability, security, and automation strategies. Resolve complex performance and scalability challenges, establish engineering standards, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a regulated environment.

Leads the architecture and engineering of secure, scalable enterprise AI infrastructure. Designs platforms for LLM training and inference, machine learning, agentic AI, and production workloads using HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, and automation. Establishes standards for reliability, security, observability, governance, and compliance; resolves complex performance challenges; evaluates emerging technologies; leads cross-functional initiatives; and mentors engineers.