Top AI Engineer Jobs

Build and lead production AI/ML, Generative AI, and data engineering solutions for healthcare analytics and patient safety platforms. Develop full-stack applications, microservices, APIs, data pipelines, dashboards, self-service analytics, RAG systems, and event-driven architectures using cloud-native technologies. Apply engineering best practices including testing, CI/CD, observability, security, and performance optimization. Mentor engineers, lead technical initiatives, and collaborate with product, analytics, business, and platform teams.

Build and operate enterprise AI platforms, semantic routing, model gateways, agentic workflows, evaluation harnesses, and production AI services. Develop secure, compliant integrations for regulated financial services, optimize AI workload economics, and establish observability, governance, and reliability standards. Prototype emerging capabilities, create reusable architecture components, mentor engineers, lead technical learning, and partner with security, risk, compliance, and data governance teams.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Defines and implements enterprise AI observability, telemetry, evaluation, drift detection, guardrails, incident management, governance controls, audit evidence, and cost monitoring. The role establishes standards for internally built and vendor AI systems, integrates monitoring and compliance platforms, maintains the AI system inventory, and coaches engineering teams on production adoption. It partners with Risk, Compliance, Legal, Privacy, and Security to operationalize AI governance in a regulated environment.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Architects and operates production-grade agentic AI systems for enterprise identity security. Responsibilities include designing multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety guardrails, observability, and human-in-the-loop controls. The role provides technical leadership through architecture ownership, code reviews, mentoring, and establishing scalable production AI practices.

Lead development of an AI agent platform using Java, Python, AWS, and React. Build cloud-native services, APIs, integrations, guardrails, observability, CI/CD automation, and production operations. Mentor engineers, lead design and code reviews, promote responsible AI-assisted development, and resolve performance and reliability issues.

Leads design and development of scalable agentic AI solutions using Java and/or Python. Builds LLM-powered systems, MCP servers, APIs, production services, and cloud infrastructure on AWS. Establishes secure coding, testing, observability, reliability, and responsible AI practices. Guides team adoption of approved AI-assisted development tools, validates AI-generated outputs, and collaborates across product, design, and operations teams to deliver compliant enterprise applications.

Installs, configures, stages, troubleshoots, and decommissions HPC and AI hardware, networking, storage, accelerators, and lab infrastructure. Maintains inventory, asset records, shipments, storage, remote access, system configurations, and audit-ready documentation. Supports lab and data-center operations, monitors safety and infrastructure conditions, coordinates with global engineering teams, and escalates technical issues. Recommends improvements to staging, workflows, automation, documentation, and operational efficiency while following safety, security, quality, and change-control procedures.

Own the vision, strategy, roadmap, adoption, and outcomes for a developer platform supporting human and AI-driven software delivery. Partner with engineering across the SDLC to improve CI/CD, automation, developer workflows, delivery speed, quality, and reliability. Prioritize platform capabilities, build internal developer communities, manage dependencies, use data to guide decisions, and influence senior stakeholders across a distributed organization.

Design, build, and evaluate conversational AI agents using Go and Python. Own agent orchestration, sub-agent architecture, prompt engineering, tool integrations, guardrails, approval flows, observability, and evaluation infrastructure. Develop datasets, scorers, regression suites, and quality metrics for production readiness. Collaborate cross-functionally on conversational experiences, make architecture decisions, rapidly prototype agents, harden them for launch, and mentor engineers.

Build and operate DigitalOcean’s managed agent runtime platform for secure, scalable AI coding agents and agentic workflows. Responsibilities include distributed services, agent framework integrations, control-plane APIs, execution orchestration, sandboxed environments, developer APIs, observability, reliability, and end-to-end feature ownership. The role requires extensive backend engineering experience, AI/LLM integration expertise, distributed systems knowledge, and experience with cloud infrastructure, orchestration, databases, and high-throughput APIs.

Develop and deploy enterprise AI/ML solutions, including GenAI, LLM, NLP, agentic frameworks, and traditional machine learning models. Build scalable Python inference APIs on Kubernetes, analyze large datasets with Python libraries and Databricks, and apply advanced SQL. Responsibilities include model evaluation, responsible AI, semantic search, performance monitoring, documentation, and use of AI-assisted development tools.

Develop secure, scalable full-stack data platforms and applied AI capabilities within an agile team. Build agentic applications, AI agents, MCP servers, and LLM-powered features using RAG, embeddings, evaluation, guardrails, and tool calling. Implement frontend and backend services, data engineering pipelines, cloud-native platform capabilities, CI/CD, observability, and controlled releases. Analyze large datasets, troubleshoot technical issues, and validate AI-assisted development outputs through testing, peer review, and secure coding practices.

Build and operate production platforms for LLM-powered agents and AI features. Responsibilities include developing reusable services, evaluating and testing LLM systems, implementing observability, applying security and governance controls, and improving AI-assisted engineering practices. The role partners with product, engineering, data, security, and compliance teams to deliver scalable solutions, with preferred experience in orchestration frameworks, RAG, vector databases, Docker, Kubernetes, AWS, and Databricks.

Design, develop, deploy, and support full stack applications with AI as a core capability. Build custom AI assistants and agents, implement prompt engineering and MCP integrations, and connect applications to LLMs, embeddings, search, and RAG systems. Own features across the software lifecycle while ensuring scalability, security, reliability, and performance. Use AI-assisted development tools to improve coding, testing, documentation, refactoring, and engineering productivity.

Design, develop, deploy, and support full stack applications with AI as a core capability. Build AI assistants, custom agents, MCP integrations, RAG solutions, and LLM-powered workflows. Develop scalable applications using C#, ASP.NET, Angular, APIs, databases, cloud-native technologies, CI/CD, containers, and Kubernetes. Own features throughout the software lifecycle while applying AI-assisted development practices and maintaining security, reliability, performance, and production quality.

Lead engineering for production-grade agentic AI and LLM platforms. Design AWS cloud-native services, RAG solutions, vector storage, agent memory, APIs, and microservices. Establish MLOps, monitoring, governance, security, observability, and responsible AI practices. Provide technical direction, make architecture decisions, review code, mentor engineers, and drive adoption of AI-assisted development standards across teams in a regulated enterprise environment.

Design and build scalable AI-native developer platforms, backend services, APIs, frameworks, and modern frontend experiences. Improve engineering productivity through AI-powered workflows, automation, developer tooling, and SDLC integrations. Lead architecture decisions, mentor engineers, and drive reliability, scalability, performance, and observability across cloud-native distributed systems. Collaborate with product managers, architects, and engineering teams to deliver reusable platform capabilities and evaluate emerging AI technologies.

Founding Full-Stack AI Engineer to architect, build, and scale an enterprise digital account manager. Own end-to-end features, build integrations and data ontology mapping, develop intuitive UIs, and design agentic LLM solutions (memory, RL). Work directly with founders and customers to iterate rapidly and deliver enterprise-grade, scalable AI products onsite in San Francisco.

Designs and operates governance, security, deployment, monitoring, and cost-management frameworks for enterprise AI platforms. The role builds semantic models in Snowflake and Databricks, develops CI/CD and infrastructure-as-code solutions, enables multi-environment deployments, monitors AI reliability and usage, and implements auditability, responsible AI standards, RBAC, identity controls, and policy enforcement. It also partners with data and engineering teams to manage lineage, governed metrics, model operations, and cloud consumption across AI workloads.

Build and operate AI, MLOps, and deployment pipelines across customer and internal environments. Lead technical integrations, prototypes, demos, and production deployments across cloud, on-premises, and hybrid systems. Debug complex infrastructure and application issues, gather customer requirements, translate field insights into product capabilities, and create technical content. Partner with customers, Product, Engineering, Sales, and Business Development while helping establish forward-deployed engineering standards.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety governance, observability, and workflow automation. The role also provides technical leadership through architecture ownership, code reviews, mentoring, and engineering standards for reliable production AI systems.