Top AI Engineer Jobs

Lead the architecture, development, optimization, and operation of production LLM systems for enterprise AI automation. Design complex capabilities including agents, retrieval, code generation, structured generation, tool use, model routing, evaluation, and observability. Drive platform modernization, reliability, scalability, architecture, and engineering excellence while mentoring engineers, influencing cross-team technical decisions, reviewing code, and documenting AI engineering practices.

Build and own AI platform capabilities across data ingestion, enterprise integrations, APIs, agent orchestration, RAG, vector search, knowledge graphs, authentication, and workflow automation. The role involves end-to-end product ownership, customer collaboration, technical architecture, rapid delivery, production support, and prompt engineering. Engineers are expected to work independently in ambiguous environments and use AI coding tools extensively to deliver secure, reliable enterprise AI products.

Build and deploy custom AI workflows, automations, and agentic systems for clients. Collaborate with executives and frontline teams to understand processes, identify AI opportunities, test solutions, measure business impact, and document reusable playbooks. Support client strategy meetings, help improve company offerings, and manage projects and communications in a fast-paced early-stage environment.

Lead the design and implementation of production agentic AI systems for enterprise compliance. Responsibilities include multi-agent orchestration, RAG and memory systems, stateful Temporal workflows, evaluation and safety frameworks, OCR and document understanding pipelines, and AWS infrastructure. The role requires hands-on Python engineering, LLM application development, distributed systems expertise, and end-to-end ownership in an in-office San Francisco startup environment.

The Senior AI Engineer will design and build AI-powered automation tools, maintain LLM systems, and collaborate with business analysts to enhance processes across various operations.

As a Senior AI Engineer, you'll design and implement AI-powered automation tools, maintain LLM-based systems, ensure data quality, and enhance business processes through AI solutions.

As a Senior AI Engineer, you will design and build AI-driven tools and pipelines to automate business processes, ensuring production reliability and integrating AI technologies into operations.

Build and operate scalable data pipelines, evaluation frameworks, and production AI/ML/NLP models. Own the full lifecycle from data collection and transformation through training, deployment, monitoring, and continuous improvement. Develop robust tooling, observability, and quality checks while collaborating with product and engineering to integrate AI capabilities into user-facing features. Work pragmatically in a fast-moving startup environment and translate business needs into effective data and machine learning solutions.

Build and improve an autonomous AI coding agent that understands tasks, generates and tests code, self-repairs, reviews changes, and integrates with developer tools. Design codebase retrieval and knowledge systems, build scalable Python services and APIs, leverage LLMs, develop agent evaluations, and identify automation opportunities with platform and product teams. The role requires strong software engineering, cloud infrastructure, CI/CD, testing, and end-to-end ownership.

Own applied AI models powering Arlo’s smart vision products, including computer vision, vision-language, retrieval, and agent systems. Responsibilities span model training, fine-tuning, evaluation, data curation, embeddings, vector search, inference optimization, production deployment, and telemetry. The role requires hands-on technical leadership, cross-functional collaboration, mentoring, and end-to-end ownership of scalable AI features across edge and cloud environments.

Lead and remain hands-on building an AI-first developer portal and secure external API platform. Define architecture for agent-ready workflows, authentication/credential flows, contract-first APIs, resiliency, observability, and AI-enabled support. Drive cross-functional delivery, coach engineers, and ensure operational controls, auditability, and production readiness for integrations.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Leads product security initiatives focused on securing AI-assisted software development, AI-enabled products, and agentic systems. Designs scalable security controls, improves vulnerability detection and remediation, partners with architecture, engineering, product, and platform teams, and provides technical direction and mentorship across PayPal.

Build and deploy AI-powered workflows, agents, automations, dashboards, alerts, and recommendations for Compute and C3 capacity operations. Integrate CRMs, internal systems, and third-party APIs; automate repetitive operational processes; troubleshoot data and migrations; audit existing tooling; and prioritize new solutions. The role requires end-to-end ownership, rapid production delivery, strong systems thinking, and fluency with AI coding assistants, workflow platforms, APIs, and webhooks.

Provides global technical leadership for Physical AI and cyber-physical product assurance. Develops standards, certification programs, assessment methodologies, testing criteria, and customer-facing services for robotics, autonomy, drones, smart mobility, and connected intelligent products. Evaluates real-world system behavior, hazards, failure modes, safety controls, human interaction, robustness, and lifecycle changes. Advises customers, regulators, and standards bodies while building global evaluator training, delivery guidance, and scalable assurance capabilities.

Provides global technical leadership for digital AI standards, certification, assurance, training, advisory, and assessment services. Develops AI governance and lifecycle assurance methodologies covering predictive and generative AI, LLMs, agents, cloud AI, model risk, testing, transparency, fairness, privacy, security, robustness, and operational resilience. Translates regulations, standards, emerging technologies, and customer needs into scalable offerings while engaging customers, regulators, standards bodies, and internal stakeholders.

The Partner Solutions Architect will drive technical engagement with systems integrators, maintaining relationships with stakeholders to ensure successful AI solutions deployment and delivering strategic guidance.

Develop and maintain scalable AI and ML architectures for a life sciences regulatory intelligence platform. Lead technical decisions, integrate LLM services and microservices, build conversational and agentic systems, manage data stores and databases, support production delivery, and collaborate in an Agile Scrum environment. The role requires hands-on Python development, generative AI deployment, cloud architecture, vector and graph databases, hybrid search, and backend API development.

Build and operate shared AI platform capabilities, APIs, SDKs, workflows, and reusable libraries for engineering teams. Develop reliable cloud-based AI and ML services using containers, orchestration, CI/CD, infrastructure as code, and strong observability. Support data preparation, deployment, monitoring, governance, security, and lifecycle management. Partner cross-functionally to productionize generative AI, retrieval, embeddings, and other machine learning use cases while improving developer enablement and operational reliability.

Leads the design, development, optimization, and deployment of AI models and software applications. Develops APIs using Python and C#, manages complex AI projects, optimizes database queries, ensures data integrity, and collaborates with cross-functional teams. Responsibilities also include technical documentation, code reviews, bug fixes, mentoring junior engineers, and delivering AI solutions that meet business and performance requirements.

Build, integrate, test, deploy, and maintain AI-enabled applications, APIs, model services, automation workflows, and user-facing tools. Translate data science prototypes into production-ready software, implement evaluation and monitoring, optimize models and prompts, and collaborate with engineers and domain experts. The role requires Python, software development fundamentals, machine learning knowledge, Git, testing, and project experience, with preferred exposure to modern AI frameworks, cloud, containers, retrieval-augmented generation, and model evaluation.

Supports business process improvement by researching workflows, collecting data, maintaining project records, developing presentations and reports, and designing AI-powered automation solutions. The intern bridges business teams and technical systems using AI tools and automation platforms. This is an on-site summer internship in Austin, Texas, with no relocation or housing assistance.

The AI AppSec Engineer V will secure AI solutions, mentor teams, and ensure the integration of security into AI development lifecycles. Responsibilities include threat modeling, code analysis, and contributing to security policies.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.