Top AI Engineer Jobs

Provide technical service and support for AI data center systems, including installation, maintenance, troubleshooting hardware and software, and collaborating with clients to ensure high-performance enterprise computing operations.

Designs, develops, deploys, and supports secure AI-powered enterprise applications and automation solutions. Builds backend services, APIs, cloud-native applications, and integrations using Generative AI, LLMs, RAG, agentic frameworks, and modern software engineering practices. Fine-tunes models, optimizes performance, manages production deployments, and contributes to architecture, documentation, Agile ceremonies, governance, security, privacy, and cross-functional delivery.

Lead Affirm’s enterprise AI security review program, threat model LLM and agentic systems, review code and configurations, and develop security guardrails, tooling, policies, and monitoring. Evaluate AI-enabled SaaS vendors, identify emerging vulnerabilities, support incident response, and lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance. The role requires expertise in AI security architecture, threat modeling, IAM, cloud infrastructure, policy-as-code, and regulated enterprise environments.

Leads the design and deployment of production-grade AI and machine learning systems for enterprise workflows and analytics. Integrates LLMs and generative AI, builds end-to-end ML pipelines, establishes AI governance and responsible AI practices, and partners with data engineering, governance, and business stakeholders. Serves as a senior technical authority, makes architectural decisions, sets development standards, mentors peers, and translates complex AI concepts for executive and nontechnical audiences.

Design and implement low-level systems software for fault-tolerant, high-performance distributed GPU training. Modify GPU frameworks and runtimes, build reliable large-scale training components, debug complex concurrent and distributed systems, and own solutions from design through production. The role requires strong systems programming, concurrency and memory-model expertise, distributed-systems reasoning, and the ability to navigate large codebases. GPU programming, high-performance networking, ML runtimes, and cluster orchestration are preferred.

Build and maintain Snowflake data pipelines, integrations, data models, reconciliation checks, backend services, and internal automation tools. Develop production AI workflows using Claude, MCP, and managed model platforms for document retrieval, extraction, and understanding. Own solutions through design, deployment, monitoring, documentation, and support while protecting sensitive financial data and collaborating with operations, compliance, vendors, and technical stakeholders.

Build and maintain Python-based automated tests for web applications, APIs, backend services, integrations, regression, and end-to-end workflows. Use AI coding and testing tools to improve coverage and efficiency. Diagnose defects, analyze results, collaborate on root-cause resolution, validate fixes, and integrate testing into CI/CD pipelines. The role supports software quality across AI applications, restaurant operations systems, and kitchen robotics.

Build and operate cybersecurity AI infrastructure supporting model training, evaluation, deployment, and serving. Develop Databricks and AWS platforms, security telemetry pipelines, GitHub Actions CI/CD, DevSecOps controls, infrastructure as code, identity and secrets management, and model evaluation workflows. Partner with AI/ML engineers, data scientists, and security operators while conducting technical reviews and guiding platform design.

Build and operate Booz Allen’s cybersecurity AI infrastructure platform using Databricks, AWS, GitHub Actions, and infrastructure as code. Develop governed security telemetry pipelines, model evaluation and serving workflows, CI/CD, DevSecOps controls, and repeatable deployments. Partner with AI engineers, data scientists, and security operators to operationalize agentic AI capabilities. Lead technical design, code reviews, platform direction, and production monitoring while enforcing least-privilege access and secure handling of sensitive data.

Own the architecture and development of PermitFlow’s AI Assistant, agentic infrastructure, workflow agents, context and retrieval systems, tool permissions, human-in-the-loop controls, and evaluation frameworks. Establish engineering standards and abstractions for agentic features while partnering closely with permit coordinators and builders to improve reliability, task completion, and customer outcomes.

Own the production deployment of neural and generative models for Waabi’s multi-sensor simulator. Responsibilities include optimizing models with compilation, quantization, mixed precision, CUDA, and custom kernels; building numerical parity benchmarks; developing model export, versioning, registry, and build tooling; collaborating with research teams; and managing GPU workloads in cloud environments.

Own the multifamily division’s AI and automation product from discovery through deployment. Build agentic workflows, Claude API pipelines, MCP integrations, evaluations, and human-in-the-loop systems while translating operational workflows into scalable products. Work independently with agents and client teams, prioritize the backlog, monitor model quality, deploy production features, and use adoption and performance data to guide iteration.

Develop and integrate embodied AI applications for robotic-assisted surgery. Design system architectures connecting edge AI, firmware, operating systems, hardware, and cloud services; build real-time sensor-fusion and robotic-control pipelines; optimize machine-learning models for embedded platforms; and lead end-to-end robotics projects. Collaborate with motion-control, perception, software, hardware, research, and product teams while maintaining safety, reliability, performance, and quality.

Build applied AI solutions from data discovery through production deployment. The role involves analyzing messy, multi-source operational and customer data using Databricks, Python, and SQL; developing pipelines, AI agents, automated workflows, lightweight applications, and knowledge tools; monitoring deployed solutions; and measuring business impact. The engineer collaborates with business and technical leaders to uncover insights, operationalize them, and demonstrate ROI from AI and automation initiatives.

Build and maintain AI services for a sovereign data lakehouse, including MCP servers, model gateways, SQL AI functions, and Spark troubleshooting agents. Develop AI platform integrations, agent frameworks, evaluation harnesses, and Kubernetes deployments. Collaborate with customer engineering teams, troubleshoot integrations, contribute to architecture, and produce tests, documentation, and release notes. The role requires production experience with LLM systems, distributed systems, and software engineering, primarily using Python or Go.

Lead AI enablement for software engineering teams by creating tutorials, prompt libraries, training programs, and adoption interventions. Develop documentation such as API references, runbooks, READMEs, and integration guides. Build AI champion networks, track adoption through metrics and dashboards, produce instructional content, and partner with engineers and leaders to improve AI-augmented development practices and technical knowledge systems.

Build and ship end-to-end product features across Rails, React, and Python. Lead execution cadence, unblock teammates, manage scope, and drive releases. Use generative AI tools for development while maintaining quality. Partner with product, design, and GTM teams to turn customer feedback into improvements. Own product areas, improve reliability and performance, and iterate rapidly based on customer usage.

Develop and maintain embedded software, reverse engineer binaries and firmware, create analysis tools and plugins, and troubleshoot hardware-supported systems. Use AI- and LLM-powered developer tools to automate reverse engineering workflows. Collaborate with cybersecurity specialists to ensure secure software. The role requires low-level software knowledge, experience with IDA Pro or Ghidra, TS/SCI clearance with polygraph, and extensive software engineering experience.

Lead design and development of high-volume, low-latency full-stack applications, collaborating with product and architecture teams. Drive microservices and API-first implementations on cloud (AWS), ensure SRE practices, define AI governance and best practices, coach and mentor engineers, document technical solutions, and facilitate communication across technical and non-technical stakeholders.

Lead end-to-end development of AI-enabled, full‑stack products and production AI Agent systems. Own architecture, APIs, observability, and deployments; collaborate with product and stakeholders; balance rapid prototyping with scalable engineering; integrate LLMs and agent loops; mentor others and ensure reliability, performance, and customer-focused UX.

Develop reusable RAG pipelines and MLOps capabilities for domain-specific knowledge repositories. Build document ingestion and processing workflows, integrate embedding models, vector databases, and LLM generation, and evaluate response quality. Automate model training, validation, deployment, monitoring, and data-source configuration. Collaborate with software developers, AI/ML engineers, and systems architects to deliver scalable, secure solutions for customer projects.

Leads the architecture, development, evaluation, deployment, and scaling of enterprise AI systems, including RAG pipelines, autonomous agents, and LLM-powered applications. Establishes AI engineering standards, guides technical strategy, evaluates emerging techniques, and partners with research, product, design, and executive teams. Drives proof-of-concept initiatives into production, mentors engineers, supports AI product strategy, and represents the organization through technical leadership and industry engagement.

Develop and maintain web applications using React Native and JavaScript, integrate AI models and LLMs, collaborate with designers and product managers, write clean code, participate in code reviews, and stay current with emerging technologies.

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.