Top AI Engineer Jobs

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Lead the architecture and development of production-grade AI solutions combining LLMs, full-stack engineering, data platforms, cloud architecture, security, and DevOps. Build AI-powered tools for meeting preparation, call summarization, workflow automation, knowledge retrieval, recommendations, and decision support. Partner with engineering, data, product, and business teams to identify opportunities, scale AI applications, and deliver measurable productivity improvements.

Build and deploy production-grade AI agents, workflow automations, and internal applications that improve enterprise processes. Partner with business, operations, engineering, and security teams to identify use cases, design reliable agent architectures, prototype with LLMs and agent frameworks, measure impact, and drive adoption. The role requires strong systems engineering, API, data, and workflow design skills, along with the ability to translate ambiguous business problems into scalable technical solutions.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Designs, tests, governs, and documents AI-assisted workflows for vulnerability and risk analysis in a federal cybersecurity environment. Responsibilities include structured prompt engineering, model parameter testing, hallucination reduction, prompt library management, quality validation, reproducibility benchmarking, and converting CISA cybersecurity workflows into standardized AI templates using enterprise Microsoft Copilot tools.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Designs, deploys, and operationalizes scalable AI microservices, MLOps workflows, production APIs, and ETL/ELT pipelines in secure cloud environments. Responsibilities include model monitoring, drift detection, automated retraining, data orchestration, CI/CD, ATO accreditation, architecture documentation, and stakeholder communication. The role supports Department of War data initiatives that improve situational awareness, operational readiness, and mission execution.

Design and build production AI, data analytics, and cloud solutions for product engineering. Own end-to-end architecture, agentic systems, evaluation standards, data pipelines, AWS platforms, APIs, and user interfaces. Develop full-stack applications, establish technical standards, mentor engineers, manage developers, and partner with engineering teams to move prototypes into adopted production systems. The role requires hands-on Python, SQL, modern front-end, LLM orchestration, RAG, data engineering, cloud, and cost-aware architecture expertise.

Build and maintain production-grade automation for real-world AI evaluations. The role owns end-to-end workflow orchestration, environment setup, artifact capture, reproducible datasets, scorecards, diagnostics, and release-triggered testing. It also develops agent- and LLM-driven workflows to evaluate customer use cases at scale, converting manual experiments into reliable standing protocols and actionable findings.

Own the architecture, administration, governance, integrations, automations, and enterprise rollout of Linear and Airtable across Salesforce’s technology organization. Build SaaS integrations, automate provisioning and operational processes, establish scalable permissions and workflows, monitor platform health, troubleshoot issues, evaluate platform strategy, create enablement materials, and mentor other engineers and administrators.

Develop AI/ML and LLM solutions by building ETL pipelines, curating evaluation data, implementing machine learning algorithms, and improving scalable infrastructure. The role collaborates with researchers and developers, supports NLP and predictive analytics, applies Agile methodologies, and may lead interdisciplinary teams. Work includes data engineering, cloud and containerized deployment, GenAI operations, and algorithm implementation across varied customer missions.

Build scalable, responsive frontend experiences for Snowflake Cortex AI. Develop production-quality TypeScript and React interfaces, integrate APIs supporting AI inference and data retrieval, participate in architecture and code reviews, collaborate with product and design teams, and optimize frontend performance and reliability. The role also involves using AI coding assistants as part of daily development and may include building LLM-powered applications or conversational interfaces.

Design, configure, and maintain enterprise AI governance platforms and workflows (evidence capture, controls, reporting). Build integrations and automations with enterprise systems, enable metrics and data quality, translate Responsible AI policies into operational controls, coordinate vendors, and deliver training and enablement to drive adoption and audit readiness.

Designs and maintains scalable data pipelines and architectures supporting AI and machine learning workloads for Army intelligence operations. The role develops data models, ensures data quality, availability, security, and governance, optimizes workflows for real-time analytics, integrates AI/ML solutions, and collaborates with internal and external stakeholders. Expertise with Palantir Foundry, programming, ETL, databases, and large language models is required, along with an active TS/SCI clearance and eligibility for TS/SCI with polygraph.

Own the architecture and technical direction of Square’s go-to-market technology stack. Design, build, and operate production AI agents and automated workflows that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations; establish AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering teams to translate business needs into scalable solutions, guide integrations and build-versus-buy decisions, and mentor engineers through influence and technical leadership.

Own the architecture and technical strategy for Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex initiatives through architecture, implementation, testing, deployment, and operations while establishing AI evaluation, observability, security, reliability, and human-oversight practices. Partner with Sales, Product, Data, and Engineering leaders to translate business needs into scalable solutions, make build-versus-buy decisions, mentor engineers, and improve business impact through data and user feedback.

Lead development of Avride’s ML infrastructure, initially owning GPU inference performance for onboard and simulation workloads. Build and optimize C++ execution frameworks, runtimes, and distributed infrastructure supporting neural network pipelines. Collaborate with applied ML teams on model architecture, diagnose performance issues, and expand ownership across broader ML pipelines. The role requires deep GPU and PyTorch knowledge, distributed systems experience, and strong expertise in multithreaded C++ environments.

Build and ship AI/ML products for finance and accounting workflows. Responsibilities include transforming messy financial data into production systems, designing evaluations, analyzing errors, optimizing quality and cost, and partnering with product and engineering. The role requires practical experience with machine learning, LLM applications, retrieval, embeddings, agentic systems, noisy data, and end-to-end production deployment.

Advise law firms on AI adoption across billing and revenue workflows. Configure Intapp Celeste AI agents, coworkers, and playbooks around billing guidelines and client terms; map work-to-cash processes; identify automation opportunities and risks; train billing and finance teams; drive adoption; and provide product strategy feedback. The role requires law firm billing or revenue operations experience, practical AI and prompt engineering fluency, strong stakeholder communication, and client-facing judgment.

Designs and implements AI-enabled business process transformations for clients. Responsibilities include prompt and context engineering, MCP tool and API integrations, agentic workflow design, knowledge architecture, documentation, evaluation, testing, governance, and human-in-the-loop controls. The role requires translating ambiguous business challenges into reliable AI systems, collaborating with executives and technical teams, and developing scalable operating models using modern AI tools.

Build and deploy production features for Marvell’s agentic AI software development lifecycle platform. Responsibilities include developing MCP integrations with engineering tools, automating CI/CD workflows, deploying containerized services on Kubernetes and AWS, writing production-quality code, measuring business impact, and collaborating with engineering and IT partners. The intern will work with LLMs, AI-assisted development tools, real users, and senior engineers.

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, RAG pipelines, vector databases, MCP tool integrations, model routing, evaluation pipelines, and governance guardrails. Translate research prototypes into secure, enterprise-scale mission capabilities while benchmarking models, optimizing performance, and supporting rapid operational integration.

Leads software development and coaches engineers while driving adoption of AI-assisted coding, agentic workflows, prompt and context engineering, and AI governance. Reviews architecture, requirements, code, testing, security, resiliency, and production changes. Establishes controls for AI-generated output in a regulated banking environment, maintains shared AI development assets, improves engineering productivity, and communicates technical progress and risks to stakeholders.

Own the architecture and technical direction of Square’s go-to-market systems. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish reliable and secure AI evaluation practices, make build-versus-buy decisions, and partner with Sales, Product, Data, and Engineering. Remain hands-on across design, implementation, testing, deployment, and operations while mentoring engineers and influencing senior stakeholders.