Top AI Engineer Jobs

Lead a team and portfolio building full-stack, cloud-native microservices and platforms. Collaborate with product managers to deliver scalable AWS solutions, mentor engineers, experiment with new technologies, and ensure regulatory needs are met using languages like JavaScript, Java, TypeScript, Python, Go, and container orchestration (Docker, Kubernetes).

Build and productionize reliable, scalable agentic AI systems (orchestration, sandboxed execution, latency optimization, agent memory, self-reflection, knowledge graphs, multimodal I/O). Implement frontier AI algorithms, ship production-grade code, mentor engineers, collaborate with ML, security, product, and UX teams, and drive roadmap and reliability improvements.

Design, evaluate, and productionize agentic AI systems and LLM-driven components to improve agent behavior, reliability, latency, and user-facing experiences. Build eval benchmarks, tune models, manage data quality, and collaborate with platform teams to deploy scalable, safe, and high-performing AI features.

Design and productionize agentic AI systems and components, improve agent behavior and reliability, create eval benchmarks and high-quality datasets, tune and evaluate LLMs and models, optimize latency and reliability in production, and partner with platform and UI teams to deliver end-to-end ML solutions.

Lead design and implementation of production agentic AI systems: agent orchestration, sandboxed execution, latency optimization, LLM-driven improvement, multimodal I/O, and platform reliability. Collaborate across ML, security, product, and UX teams, mentor engineers, and productionize frontier AI algorithms at scale.

Build and productionize agentic AI systems: improve agent behavior, reliability, latency, and quality using LLMs, multimodal models, and vector databases. Design eval benchmarks, manage training and evaluation datasets, implement context engineering, orchestration, multilingual and multimodal support, and partner with platform teams to deliver production-grade, tested ML-driven features.

Design, build, and scale reliable agentic AI systems and infrastructure (orchestration, sandboxed execution, latency optimization, memory, multimodal I/O). Productionize frontier AI algorithms, collaborate with ML/security/product teams, mentor engineers, and deliver production-grade, performant code that advances enterprise AI assistant capabilities.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale features, optimize performance, review code, address technical debt, and collaborate cross-functionally. Participate in weekday, weekend, and occasional nighttime Tier 2 or Tier 3 on-call rotations.

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.

Design, build, operate, and maintain enterprise AI platform services and agent runtimes. Develop APIs, SDKs, MCP servers, tool interfaces, retrieval and embedding pipelines, model serving, evaluation infrastructure, and distributed data foundations. Establish engineering, security, testing, CI/CD, and infrastructure standards while optimizing platform cost and scale. Apply AI/ML capabilities to healthcare workflows, ensure PHI protection and tenant isolation, evaluate emerging technologies, and mentor engineers while influencing technical strategy.

Lead the architecture and end-to-end development of production-grade AI/ML systems, including training, deployment, monitoring, MLOps, deep learning, NLP, ASR, and agentic AI workflows. Translate research into scalable enterprise solutions, establish engineering and governance standards, evaluate emerging tools, communicate technical tradeoffs to leadership, and mentor engineers. The role requires expertise in Python, deep learning frameworks, cloud ML platforms, ML pipelines, statistics, and production LLM or agentic systems.

Lead design and implementation of AI Ops solutions (RAG, agentic workflows) with enterprise reliability and responsible AI. Improve SRE practices, lead incident response, automate infrastructure with Terraform and GitHub Actions, operate Kubernetes clusters, and develop software using Python and Node.js while mentoring globally distributed teams.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Build and operate multi-cloud platform automation across AWS, GCP, and Azure. Develop Terraform modules, CI/CD components, Python automation, infrastructure provisioning, lifecycle management, drift detection, dependency scanning, and remediation workflows. Integrate AI-assisted and agentic tooling into operational processes while establishing security guardrails. Collaborate with Security, IAM, Network, and FinOps teams on identity, secrets management, least-privilege controls, and cloud governance.

Secure LLMs, generative AI systems, ML infrastructure, training pipelines, and AI applications. Responsibilities include threat modeling, architecture reviews, security controls, hardening, vulnerability remediation, data privacy, incident response runbooks, vendor reviews, governance, policy development, and AI security training. The role also supports proof-of-concept security solutions and monitors emerging AI threats.

Evaluates and secures AI tools, SaaS applications, marketing technology platforms, integrations, and third-party plugins. Conducts secure code reviews, SAST/DAST testing, threat modeling, architecture assessments, and vendor risk reviews. Develops AI security standards, reference architectures, policies, and playbooks while partnering with engineering, procurement, legal, marketing, and digital teams. Supports compliance with security and privacy requirements and coordinates risk, compliance, and audit initiatives.

Leads the Solutions Architecture team supporting Cloudflare’s AI and Developer Platform. Develops technical sales strategy, drives pipeline and revenue, manages people and budgets, advises sales leadership, engages executive customers, collaborates with Product and Marketing, and establishes customer feedback loops to improve product strategy and adoption.

Conduct AI security research, agentic and LLM testing, vulnerability discovery, adversary simulations, and full-chain red team operations. Test WAF, EDR, and SIEM detection efficacy using MITRE ATT&CK, collaborate with blue teams and incident response, develop detection validation frameworks, report risks to executives, and mentor security professionals.

Lead design and deployment of physics-based, ML, and LLM-powered modeling and agentic AI systems for semiconductor process, packaging, and manufacturing workflows; architect integrations with design automation and fab/test platforms; benchmark and improve models; and translate results into actionable insights and visualizations.

Design, develop, and maintain scalable full-stack AI applications across frontend, API, and backend layers. Translate business requirements into secure, reliable solutions and own the complete software lifecycle from design through support. Build AI-native systems using LLMs, retrieval-augmented generation, agentic workflows, and automation. Collaborate across teams, resolve performance and reliability issues, improve engineering standards, and develop reusable cloud-native platforms for enterprise-wide use.

Design, develop, and maintain scalable full-stack applications across frontend, API, and backend layers. Build AI-native systems using LLMs, retrieval-augmented generation, agentic workflows, and intelligent automation. Translate business requirements into secure, reliable solutions; manage the full software lifecycle; resolve performance and stability issues; and collaborate cross-functionally to deliver enterprise-scale applications.

Build and operate production AI applications and shared platform capabilities using AWS Bedrock and Python. Develop AI agents, MCP servers, APIs, and distributed services; integrate AI into existing workflows; evaluate retrieval, tool-use, and orchestration approaches; and improve reliability, security, observability, and maintainability. Partner with technical and business stakeholders to identify opportunities, guide technical decisions, support delivery planning, and mentor less experienced engineers.

Leads the design, development, and integration of scalable AI solutions, including agentic AI and multi-agent systems. Provides technical leadership, manages AI project lifecycles, collaborates with product and business teams, and mentors AI engineers. The role requires expertise in machine learning, cloud AI platforms, LLMOps, and related technologies while working hybrid in Phoenix, Arizona.

Provide technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive modeling, and optimization. Design scalable AI platforms, APIs, infrastructure, data pipelines, and observability frameworks. Lead architecture through deployment and continuous improvement while optimizing performance, reliability, security, and cost. Partner with multidisciplinary teams, establish engineering standards, mentor engineers, and translate emerging AI capabilities into measurable business outcomes.

Leads enterprise AI tooling and security engineering, designing scalable cloud platforms and secure AI applications. Responsibilities include protecting models, agents, prompts, data, APIs, and outputs; securing the AI/ML lifecycle; implementing identity, encryption, monitoring, threat detection, adversarial testing, and incident response; and establishing engineering standards. The role mentors engineers, influences architecture and technology strategy, and partners with governance, risk, compliance, and business teams to support responsible AI adoption.