Top AI Engineer Jobs

Leads the design, development, and maintenance of scalable machine learning platforms and infrastructure. Builds tools for model training, deployment, monitoring, and lifecycle management; integrates data engineering and model-serving capabilities; develops secure APIs and automation pipelines; and improves reliability, scalability, and performance. Partners with data scientists, ML engineers, and product teams while driving MLOps, CI/CD, infrastructure automation, and AI-assisted engineering practices.

Build and ship production infrastructure for reliable AI agents handling complex, multi-step tasks. Responsibilities include agent execution, retrieval and memory systems, evaluation harnesses, production improvements, customer collaboration, orchestration, and hiring. The role requires owning the full development cycle and improving agent performance using real-world feedback.

Develop AI-assisted engineering practices and shared tooling for physicists and engineers. Establish standards for LLM use in embedded C++, Rust, RTOS, and kernel development, while measuring productivity, quality, safety, and reliability. Influence AI tooling architecture and workflows across a multidisciplinary engineering organization, supporting experiment analysis, optics, PCB and FPGA design, quantum control software, and safe AI operation in quantum processors.

Build and deploy AI-powered products and workflow automation for home healthcare. Responsibilities include developing agents and intelligent workflows, backend services, APIs, data pipelines, document intelligence systems, evaluations, monitoring, and production AI components. Partner with clinical, product, design, and operations teams to understand workflows, improve system quality through feedback and error analysis, and determine when to use AI, deterministic software, or human review.

Own the design, development, deployment, evaluation, and monitoring of production AI systems for healthcare workflows. Build reliable agents and copilots that reason over structured and unstructured data, integrate tools, handle uncertainty, and escalate to human reviewers when needed. Partner cross-functionally with product, clinical, operations, and engineering teams, while mentoring engineers and shaping reusable Applied AI platform capabilities.

Designs, builds, deploys, and operationalizes enterprise generative AI solutions, including LLM applications, RAG architectures, autonomous agents, MCP servers, and AI integrations. Partners with stakeholders, engineering, infrastructure, security, and data teams to deliver secure, governed, scalable AI systems. Develops AI tools and workflows, optimizes data processes, provides architectural guidance, and supports high-impact business use cases.

Leads the design, development, deployment, and optimization of AI-powered marketing workflows. Owns the Marketing AI and Automation backlog, designs LLM, RAG, and multi-agent systems, builds data foundations, and operationalizes secure, observable production solutions. Partners with marketing, Martech, analytics, and leadership teams to define use cases, metrics, integrations, guardrails, and enablement strategies.

Designs and builds scalable, secure AI/ML solutions for healthcare clients, including LLM workflows, AI agents, copilots, and intelligent automation. Leads solution architecture, prototyping, cloud deployment, evaluation testing, troubleshooting, and production delivery. Translates ambiguous business and clinical needs into actionable technical solutions while advising stakeholders and collaborating across product and engineering teams.

Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment.

Lead AI/ML strategy and implementation for enterprise Engagement Technology Operations. Drive AIOps, generative AI, intelligent automation, observability, predictive monitoring, anomaly detection, automated remediation, and self-healing capabilities. Oversee platform reliability, incident response, disaster recovery, governance, vendor relationships, and operational transformation. Partner with business and technology leaders to define roadmaps, measure outcomes, communicate risks, and improve scalability, resilience, efficiency, and cost optimization across critical platforms.

Owns enterprise AI security assessment, architecture, and control programs. The role evaluates AI systems, vendors, platforms, and shadow AI; designs secure architectures, guardrails, identity models, sandboxes, gateways, and runtime controls; and develops detection and response capabilities for prompt injection, jailbreaks, data leakage, and tool misuse. The engineer partners with governance, identity, platform, engineering, and security operations teams, advises senior leaders, drives remediation, and mentors security professionals.

Analyze and optimize AI training and inference workloads across distributed systems. Design benchmarks, profile performance, identify bottlenecks, and optimize communication software using MPI, NCCL, UCX, libfabric, and RDMA. Translate modeling and benchmarking results into validated system improvements while coordinating technical work across hardware, software, networking, and architecture teams.

Leads performance optimization for large-scale AI training and inference workloads across distributed systems. Builds benchmarks, profiles workloads, identifies compute, memory, networking, and software bottlenecks, and develops C++ and Python tools. Coordinates complex performance improvements across hardware and software teams, validates results with reliable data, and guides engineering decisions that improve efficiency, scalability, and reliability at data center scale.

Lead performance strategy for large-scale AI training and inference systems. Investigate workload behavior, profiling, benchmarking, modeling, distributed communication, and multi-node scaling across hardware and software. Define measurement standards, guide optimization roadmaps, review C++ and Python tools, and influence system architecture and engineering priorities across teams.

Build and deploy governed AI applications for Clayco employees, including retrieval over internal procedures, training content, project history, and documentation. Translate domain models into effective retrieval boundaries, evaluate AI approaches against real work tasks, establish prompt and context engineering standards, and integrate applications with the company’s agent registry and identity model. The role requires production experience with retrieval and document intelligence, close collaboration with domain experts, and comfort working in an early-stage AI program.

Develop and integrate intelligent feature layers for Workday’s AI agents. Build production-grade LLM applications using RAG pipelines, orchestration frameworks, prompt engineering, APIs, vector databases, semantic search, and data pipelines. Partner with engineering, product, and UX teams to deliver reliable, user-focused AI features embedded in Workday’s platform. Responsibilities include backend development, testing, deployment, data integration, and continuous optimization of agentic workflows.

Build and operate partner integrations enabling secure agent experiences across web applications, APIs, connectors, CLIs, and MCP servers. Responsibilities include architecture, coding, identity and OAuth debugging, evaluations, launch readiness, staged rollouts, observability, incident investigation, and platform improvements. The role requires close collaboration with external engineering teams and internal product, security, legal, and go-to-market stakeholders.

AI Engineering Intern supporting the design, development, testing, and responsible use of AI-enabled solutions. Responsibilities include coding, data preparation, AI prototyping, automation, cloud and platform support, testing, documentation, governance, and communicating technical findings. The 10-week, full-time hybrid internship runs from June through August in Reston, Virginia, with in-person work twice weekly.

Supports rapid AI prototype development using AI tools and frameworks, building lightweight front-end and back-end interfaces, designing prompts, applying responsible LLM grounding techniques, and integrating and testing capabilities in real workflows. Collaborates with senior engineers and technology partners, learns emerging AI technologies, and may present deliverables to leadership. This is a full-time, 10-week hybrid internship in Reston, Virginia.

Design and deploy autonomous multi-agent AI workflows integrating LLMs with enterprise databases, APIs, and long-term memory. Build orchestration, RAG, evaluation, observability, guardrails, and production microservices using scalable asynchronous patterns. Develop secure, reliable agentic systems with human-in-the-loop escalation and enterprise search integrations. Collaborate on NLP, AI integration, search relevance, contact center platforms, and customer-focused solutions.

Own the developer documentation and learning experience for Intel’s AI software stack. Create golden paths, tutorials, reference materials, samples, and structured learning curricula. Build docs-as-code workflows and documentation CI, validate Python-based AI samples, optimize content for search and AI assistants, analyze documentation usage, and partner with engineering, product, and developer advocates to ensure new capabilities launch with tested, discoverable documentation.

Design, develop, and deploy production AI, machine learning, and generative AI systems. Build training and inference data pipelines, implement algorithms in Python, and operationalize models with MLOps, CI/CD, and containerization. Develop APIs and microservices, optimize models for performance and scalability, and ensure AI governance and ethical compliance. Collaborate with engineering teams to deliver secure, production-grade GenAI applications.

Build and own production LLM-powered healthcare applications, including clinical trial matching, ambient copilots, billing automation, documentation, inference pipelines, and real-time speech recognition. Collaborate with product, design, clinical, and go-to-market teams to rapidly launch features, gather user feedback, and improve workflows used by clinicians. The role requires strong AI engineering, product judgment, and full-stack execution skills.

Leads hands-on design, development, deployment, and support of enterprise agentic AI applications. Designs multi-agent workflows, RAG pipelines, vector search, MCP integrations, and LLM solutions. Provides technical leadership, code reviews, mentorship, and architectural guidance while establishing engineering standards for CI/CD, testing, security, scalability, and responsible AI. Partners with executives and stakeholders to translate AI strategy into delivery plans and evaluates emerging AI technologies.

Build and operate production AI-powered developer tools, including code review assistants, test generators, deployment diagnostics, and incident-response agents. Develop the self-service platform supporting deployment pipelines, observability, security, cost management, and reliable AI systems. Integrate LLM providers, agent frameworks, MCP servers, CI/CD, and infrastructure platforms. Own features from design through production, participate in on-call, improve operational practices, collaborate cross-functionally, and mentor engineers.