Top AI Engineer Jobs

Lead DigitalOcean’s AI security strategy, governance framework, threat modeling, and multi-year roadmap. Partner with security, engineering, and data science teams to secure AI products, deploy guardrails, conduct adversarial testing and red teaming, and monitor AI/ML systems. Provide architectural reviews, executive risk briefings, and guidance on emerging threats including prompt injection, model supply-chain risks, agentic AI, and data exfiltration.

Performs manual and automated testing for web, API, backend, and AI-enabled applications. Develops and maintains automation frameworks, executes regression suites, supports CI/CD integration, validates AI outputs, and creates test plans and cases. Tests functional, integration, system, smoke, API, UI, database, and backend workflows. Tracks defects, reports QA metrics, collaborates with Agile teams, and identifies opportunities to improve testing efficiency through AI-powered tools.

Develop and optimize Pacsun’s enterprise MicroStrategy environment, including schemas, semantic layers, dashboards, dossiers, reports, and performance tuning. Integrate generative AI, natural-language queries, predictive analytics, anomaly detection, and RAG-enabled use cases. Write complex Snowflake SQL, support ETL/ELT processes, enforce governance and security, troubleshoot platform issues, document solutions, collaborate with business stakeholders, and mentor junior developers.

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

Lead DigitalOcean’s AI security strategy, governance framework, roadmap, and organizational programs. Build and operationalize AI/ML security controls, tooling, guardrails, and automation; conduct threat modeling, adversarial testing, and AI red teaming. Review AI architectures, guide high-risk product decisions, monitor deployed models, and address risks involving RAG, agents, model supply chains, and prompt injection. Represent AI security to executives and external communities while translating emerging research and threats into engineering guidance and policy.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct threat modeling, adversarial testing, AI red teaming, and security reviews; develop guardrails and automation with engineering teams. Advise executives and the board on AI risk, define organizational controls, and guide responses to emerging threats including prompt injection, model supply-chain risks, and AI-enabled social engineering.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct AI red teaming, threat modeling, security reviews, and adversarial testing, while building defensive tooling and guardrails. Advise executives and the board on AI risk, guide engineering teams, and define organizational policies for responsible AI security.

Lead DigitalOcean’s AI/ML security strategy, architecture, governance, and multi-year roadmap. Build security controls, guardrails, tooling, and automations; oversee AI red teaming, adversarial testing, threat modeling, and model monitoring. Review AI product architectures, guide high-risk decisions, establish responsible AI policies, and brief executives and the board on AI security risks. Serve as the internal expert and external representative on emerging AI security topics, including agentic AI, model supply chains, prompt injection, and data integrity.

Leads enterprise full-stack software engineering for secure, scalable managed-account platforms. Owns application components across architecture, development, testing, deployment, observability, and production support. Designs distributed cloud-native systems using Java and modern frontend technologies, mentors engineers, improves CI/CD and test automation, and drives responsible adoption of AI-assisted development tools. Partners with cross-functional stakeholders to deliver high-impact initiatives in an agile environment.

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Design, develop, and operationalize AI and machine learning solutions for production applications and data workflows. Build data and model pipelines, implement MLOps and LLMOps practices, support generative AI and RAG systems, and contribute to CI/CD, observability, responsible AI, governance, and model lifecycle management. Collaborate with data science, software engineering, and cloud teams in secure federal environments.

Leads software engineering efforts within an agile infrastructure platform team. Develops, reviews, debugs, and governs secure production code using Java full-stack technologies, React, cloud platforms, containers, and relational databases. Drives adoption of AI-assisted engineering tools, establishes validation and security standards, coaches technical teams, promotes SDLC automation, and influences technology decisions. Balances hands-on development with technical leadership and contributes to an inclusive engineering culture.

The HPC & AI Performance Engineer will execute benchmarking projects, analyze performance, and optimize HPC systems using scientific software and methods.

Build the AI platform enabling scientists and agents to collaborate on therapeutic research. Develop agent execution, model inference, and scientific tool abstractions; create products that accelerate experimentation and decision-making; and partner with scientists to define workflows across people, agents, and models. The role requires strong software architecture, product judgment, and practical understanding of agent reliability and evaluation.

Build and operate production AI agent systems for IT services. Responsibilities include developing integrations, APIs, agent workflows, agent harnesses, backend systems, data pipelines, and model-adjacent infrastructure. The role involves embedding with portfolio teams, translating ambiguous field problems into reliable software, partnering with stakeholders, scaling deployments, and designing evaluations to improve agent quality. This is an in-office position in San Francisco or New York City.

Deploys and sustains secure Linux-based infrastructure for AI/ML and LLM applications. Manages containerized environments, cloud deployments, CI/CD workflows, automation, enterprise integrations, and security configurations. Troubleshoots complex production environments and collaborates with software, platform, AI/ML, and cybersecurity teams. Develops operational tooling, technical documentation, deployment procedures, and runbooks while supporting reliable AI platform capabilities in secure enterprise environments.

Develop and integrate AI/ML capabilities for satellite mission planning, space domain awareness, and resilient operations. Responsibilities include end-to-end machine learning, sensor-data classification and tracking, anomaly detection, scalable HPC pipelines, modeling and simulation integration, algorithm optimization, and technical assessments. The role requires collaboration with satellite designers, operations planners, and subject-matter experts in a fully onsite classified environment.

Own the architecture and operation of a secure, scalable GCP platform for production AI agents. Design networking, identity, service perimeters, secrets management, Terraform modules, CI/CD pipelines, LLM access, observability, and cost controls. Lead incident response, postmortems, provider relationships, and platform governance while enabling product teams to deploy independently and safely.

Partners with sales executives to provide technical pre-sales support, lead proof-of-concept processes, deliver customer presentations, architect cloud security solutions, and help close opportunities. Maintains product expertise, supports events and technical working sessions, updates CRM records, collaborates with sales and engineering, and contributes to revenue targets. Requires extensive sales engineering and cloud security experience, strong presentation skills, and familiarity with major cloud platforms and security solutions.

Designs, develops, and maintains production-ready AI/ML applications for intelligence missions, focusing on LLM integration, agentic workflows, and RAG pipelines. Builds Python services, REST APIs, backend components, database workflows, and enterprise integrations. Optimizes AI systems for performance, scalability, reliability, and usability while evaluating model effectiveness and applying software engineering best practices. Requires collaboration with technical stakeholders to deliver mission-focused capabilities.

Design and build a scalable agentic AI framework integrating MCP, A2A communication, machine learning, data pipelines, APIs, and orchestration technologies. Develop SDKs and tooling, support secure multi-tenant deployments, evaluate emerging AI and LLM technologies, and mentor engineers. The role requires production-grade AI and data platform experience, strong Python skills, distributed systems knowledge, API development, Kubernetes, Docker, security, IAM, and enterprise AI architecture expertise.

Leads technical strategy and hands-on delivery across data platforms, engineering, analytics, AI, machine learning, governance, and automation. Manages roadmaps, backlogs, technical programs, and internal data and AI products while coordinating teams, vendors, and stakeholders. Develops prototypes, analyses, integrations, dashboards, and automations; evaluates technical risks and performance; builds business cases; tracks KPIs and value realization; and supports budgets, investments, planning, and strategic priorities for the Data and AI organization.

Leads adoption of AI-assisted engineering practices across product teams. Builds reusable prompts, agent workflows, integrations, validation systems, sandboxes, and AI-enabled developer tooling. Partners with platform and engineering teams to integrate AI into CI/CD, testing, observability, release processes, and product capabilities. Coaches engineers, establishes governance and security controls, and measures productivity, quality, delivery, adoption, and cost outcomes.

Develop and maintain scalable, high-performance full-stack web applications and AI-driven solutions. Build front-end and back-end components using Python, Java, JavaScript, Angular, TypeScript, React, Node.js, Spring Boot, FastAPI, and multiple databases. Develop APIs and integrate authentication protocols, data stores, and AI/ML frameworks. This is a long-term, fully onsite contract role in Dallas, Texas.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.