Top AI Engineer Jobs

Leads the design and implementation of large-scale, cloud-native full-stack systems for cloud operations resilience. Develops distributed microservices using Python, Go, JavaScript, TypeScript, AWS, Docker, and Kubernetes. Defines platform roadmaps, architecture, engineering standards, and operational practices while improving reliability, performance, and technical debt. Mentors engineers, leads design reviews and cross-functional initiatives, evaluates emerging AI technologies, and influences enterprise technology strategy.

Build and lead development of cloud-based, full-stack systems supporting observability, connectivity, resilience, and availability. Design distributed microservices, define service boundaries, establish engineering standards, improve system performance, reduce technical debt, and implement AI-powered solutions. Collaborate with product managers and architects, influence technology strategy, mentor engineers, and drive cross-functional technical initiatives using Python, Go, JavaScript, TypeScript, AWS, Docker, and Kubernetes.

Leads the architecture, development, deployment, and optimization of large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establishes enterprise AI architecture, performance and safety standards, and multi-year platform strategies. Partners across engineering, research, product, and program management while mentoring technical leaders and driving responsible AI adoption.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration pipelines, and observability. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentorship while improving model scalability, latency, throughput, hardware utilization, and inference costs.

Design, develop, deploy, and support scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across distributed cloud platforms. Own AI system architecture and reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform roadmaps, and mentor engineering staff.

Design, develop, deploy, and support scalable AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability objectives, governance, security, and compliance while collaborating across engineering, research, product, and program teams. Lead technical reviews, shape foundational AI roadmaps, and mentor senior technical staff.

Build and deploy production AI agents using Python, FastAPI, AWS Bedrock, AgentCore, and Strands Agents. Develop MCP tools, RAG pipelines, prompts, model evaluations, and guardrails. Ship REST and WebSocket features backed by PostgreSQL and Redis, contribute to background jobs, test code, review pull requests, and document architectural decisions. This fully in-office early-career role emphasizes mentorship, fundamentals, curiosity, and progressively expanding ownership.

Design, build, and deploy generative AI and LLM-powered applications and ML pipelines. Implement prompt engineering, fine-tuning, RAG and vector retrieval, MLOps (CI/CD, Docker), model serving, and APIs. Partner with stakeholders to translate business needs, evaluate model performance, and mentor junior engineers while delivering production AI solutions.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

As a Senior/Principal Machine Learning Engineer, you'll design and build core ML systems for Workday's AI agents, collaborating with cross-functional teams on models and deployment for real-world applications.

Leads the design, development, testing, deployment, and operational support of production applications incorporating AI, automation, APIs, cloud technologies, and event-driven services. Owns features end to end, translates ambiguous requirements into practical solutions, collaborates with mission stakeholders, establishes reusable development patterns, and mentors or guides technical teams supporting national security missions.

Build and deploy generative AI solutions for sales and marketing, including LLM-powered agents, RAG pipelines, CRM integrations, real-time AI workflows, streaming APIs, and model evaluation infrastructure. Rapidly prototype full-stack AI tools that improve lead enrichment, outreach, CRM automation, and live sales-call support. The role requires strong Python or TypeScript development, LLM orchestration expertise, production software practices, and a product-oriented understanding of revenue operations.

Build and operate AI-native applications and agentic systems end to end, from stakeholder discovery and prototyping through deployment, adoption, and ongoing iteration. Design retrieval, context, tool integrations, orchestration, evaluation, observability, and cost controls. Develop scalable event-driven systems, collaborate with platform and product teams, document solutions, train users, and mentor less experienced engineers.

Build and deploy enterprise AI applications on Snowflake, including copilots, RAG systems, semantic search, AI agents, and intelligent workflows. Explore enterprise data, develop scalable APIs and pipelines, evaluate and monitor AI quality, implement responsible AI controls, and translate business challenges into production-ready solutions. Partner with technical and executive stakeholders to establish AI architecture standards and deliver measurable business value.

Design and build scalable Go or Rust backend services and data pipelines for asset discovery, recognition, and enrichment. Solve distributed-systems challenges involving concurrency, fault tolerance, and high-throughput processing. Integrate AI and ML capabilities, use AI-assisted development tools, contribute to architecture and technical standards, mentor engineers, and participate in code reviews and continuous improvement.

Build and operate internal AI agents and automations for company-wide workflows, including knowledge management, onboarding, access provisioning, vendor reviews, security questionnaires, contracts, billing, and reporting. Integrate LLMs with business systems using Python or TypeScript, establish permissions and audit trails, manage a skill library, improve data sources, measure adoption and cost, and work directly with internal teams to automate manual processes.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Combines wastewater process engineering with AI, machine learning, process modeling, digital twins, and optimization. Responsibilities include analyzing treatment data, developing biological and chemical process models, evaluating and troubleshooting facilities, conducting experiments, testing technologies, and implementing predictive or optimization solutions. The engineer collaborates with multidisciplinary project teams and utility clients to improve treatment performance, reliability, energy use, and operational decision-making. Python programming and wastewater modeling platforms are preferred, along with wastewater treatment, nutrient removal, data science, and technical communication expertise.

Develops production solutions using Power Apps, Power Automate, Dataverse, SQL, and AI agents. Responsibilities include designing applications, workflows, integrations, secure APIs, database schemas, and Copilot Studio agents; implementing ALM and CI/CD with Azure DevOps; and supporting documentation, training, troubleshooting, and production operations.

Build autonomous AI agent systems for national security applications, including cognitive architectures, multi-agent orchestration, memory, planning, post-training, fine-tuning, evaluation frameworks, and deployment optimization. Own feedback loops from operational data and collaborate with platform and forward-deployed engineers. The role requires production-grade software and ML systems expertise, extended-horizon agent experience, model optimization skills, and sensitivity to adversarial or classified environments.

Build AI-assisted, production-ready full-stack applications from concept and prototype through deployment, with a strong frontend focus. Develop accessible React, Vue, and Nuxt interfaces aligned with design systems, implement backend APIs and services, manage SQL and NoSQL databases, and optimize authentication, caching, performance, and scalability. Collaborate with designers and innovation teams while using AI design tools, Figma, specification-driven workflows, and cloud deployment practices.

Build and operate enterprise data and AI systems, including ELT, streaming, semantic, embedding, indexing, vector search, and RAG pipelines. Develop safe tool interfaces for agents, implement data products and contracts, and establish quality, observability, evaluation, and governance practices for AI workloads. Collaborate with architects, data scientists, AI engineers, governance teams, and domain engineers to productionize AI use cases and reusable platform patterns.

Designs, develops, deploys, and operates AI capabilities for enterprise and facilities-management platforms. Responsibilities include building intelligent assistants, RAG and semantic search solutions, document intelligence workflows, AI APIs, data pipelines, prompt and retrieval strategies, and evaluation frameworks. The role also covers Azure cloud deployment, CI/CD, MLOps, monitoring, security, governance, and responsible AI practices.

Improve the quality and reliability of Glean’s AI assistants and agents through evaluation-driven development, model routing, retrieval, prompting, and post-training. Build supervised and preference-optimized models, reinforcement-learning environments, evalsets, LLM judges, and agentic verifiers. Analyze production traces, calibrate evaluation signals against human graders, balance quality with latency and cost, and partner with product, platform, research, and customer teams to ship production ML improvements.