Top AI Engineer Jobs

Design and deploy agentic AI systems that automate multi-step financial workflows. Architect agents using frameworks like LangChain and LlamaIndex, apply prompting, RAG, and fine-tuning, optimize reasoning and tool use, build feedback-driven systems, and integrate AI reasoning securely into production backend and frontend services.

As a Knowledge Development Engineer, you'll architect and deploy AI-driven production systems, reverse-engineer existing systems, and guide product development. You'll synthesize customer feedback and lead experiments to connect technical development with business outcomes.

The Knowledge Development Engineer leverages AI agents to solve customer problems by building applications and automating workflows, focusing on delivery speed and quality.

Develop and maintain AI tooling systems for space stations, focusing on frontend customizations, AI integration, and full-stack feature implementation.

Design, build, and scale an AI-native conversational platform powering autonomous customer agents. Own full-stack systems, retrieval and orchestration, observability, and multi-channel experiences. Lead technical decisions, mentor engineers, and deliver reliable, tested, customer-facing features that enable brands to launch and improve configurable AI agents.

Lead and grow a team building Klaviyo's Customer Agent conversational AI platform. Set technical direction, own roadmap, collaborate cross-functionally, guide system and model design, ensure reliability, hire and develop engineers and managers, and deliver scalable, AI-driven customer engagement features.

Owns AI-assisted engineering across accelerator software teams, embedding agentic workflows into coding, debugging, triage, testing, documentation, CI, and silicon-debug processes. Builds shared agent tooling, maintains evaluations, measures productivity gains, and establishes standards for reliable AI-native development. Requires expert experience with Codex, Claude Code, LLM APIs, evaluations, and adopted developer tooling, plus strong systems or infrastructure credibility.

Own cross-team, platform-level engineering initiatives end-to-end: architecture, migrations, observability, on-call, and customer outcomes. Drive AI-native tooling and agentic AI adoption, mentor senior engineers, and set technical standards across teams.

Build research infrastructure and tooling that enable AI models to design semiconductor chips. Responsibilities include developing reinforcement learning environments, integrating EDA tools, creating evaluation and model harnesses, improving experiment reliability and observability, debugging infrastructure, and converting successful research experiments into reusable systems and training workflows.

Build and operate production-grade AI, data, and distributed systems for fraud intelligence. Responsibilities include real-time data pipelines, high-throughput architecture, LLM-powered agentic workflows, AI tooling, rule and label automation, ML pipeline productionization, privacy safeguards, and cross-functional collaboration. The role requires strong Python engineering, distributed systems, cloud infrastructure, big data technologies, and machine learning fundamentals.

Own and operate the company’s enterprise AI platform across Google Cloud, Claude Enterprise, and Gemini Enterprise. Responsibilities include AI platform operations, CI/CD, infrastructure automation, observability, identity and user lifecycle management, marketplace extensions, internal agents and tooling, AI governance, evaluation, onboarding, documentation, and enablement. The role requires 3–5 years of platform, DevOps, SRE, MLOps, backend, or infrastructure experience and is onsite four days weekly in Chicago.

Build OpenArt’s enterprise AI platform for teams and organizations. Responsibilities include developing workspaces, RBAC, SSO/SAML, audit logging, usage-based billing, invoicing, payment integrations, scalable APIs, multi-tenant architecture, and polished React/Next.js administration experiences. The role also involves improving security, data isolation, reliability, and SOC 2 groundwork; partnering with founders, sales, and customers; making infrastructure decisions; and establishing enterprise engineering practices as the first hire.

Build infrastructure for large-scale AI and agentic workloads, including synthetic data generation, evaluation, orchestration, distributed compute, simulation environments, LLM routing, observability, and developer tooling. The role requires operating nondeterministic AI systems in production, improving reproducibility and reliability, and leading complex engineering initiatives across research, product, and engineering teams.

The role involves architecting AI applications, delivering scalable solutions, conducting research, and implementing high-quality code for cloud platforms. Requires collaboration and effective communication.

As a Software Engineer at Paces, you will build AI automation solutions to expedite power development, working closely with industry experts and taking ownership of projects to enhance scalability and features.

Develop and deploy geospatial AI/ML capabilities for DoD and intelligence customers. Apply machine learning, data analytics, computational modeling, clustering, and statistical methods to structured and unstructured data to generate predictive and prescriptive intelligence insights. Integrate diverse data sources, optimize analytical workflows, support operational missions, lead projects, and translate research and commercial practices into actionable solutions for intelligence analysis and resource allocation.

Design, implement, and train reinforcement learning and multi-agent algorithms; build scalable Python-based training pipelines and simulated environments; run distributed training with cloud and container orchestration; transition models to production, optimize system performance, and collaborate with domain experts to align rewards and evaluations with mission objectives.

Design, build, and deploy agentic AI systems and multi-agent workflows to detect anomalies, fraud, bot attacks, API abuse and network threats using large-scale telemetry, RAG, knowledge graphs, model monitoring, and security integrations. Provide technical leadership, mentor junior engineers, and define standards for AI agent development.

Develop and deploy scalable, production-ready AI and machine learning models for protein, antibody, and broader drug discovery. Collaborate with AI scientists, engineers, and drug discovery experts to translate research concepts into robust systems, support large-scale model training, and deliver measurable impact in life sciences and healthcare.

Develop and maintain XR products and spatial UIs to interface with humanoid robots and data-capture hardware. Implement features, fix bugs, harden XR data sync, integrate custom hardware with XR headsets, and support hardware experiments, low-latency video streaming, and motion-capture workflows.

Recognized as a senior security expert, this role independently resolves complex cybersecurity challenges, defines methods for new assignments, provides strategic direction, and collaborates across teams. The engineer leads high-impact security projects, develops innovative solutions aligned with business needs, and drives enterprise-wide improvements to security practices and operational efficiency.

Supports stakeholders in identifying AI adoption opportunities, designing and refining prompts, building AI-enabled workflows, validating outputs, and implementing practical generative AI solutions. The role includes requirements gathering, workflow automation, RAG and agentic AI exploration, prototyping, documentation, demonstrations, user training, and collaboration with technical teams on deployment and integration.

Supports an intelligence mission team by defining, documenting, and validating requirements for AI-enabled enterprise applications. Collaborates with customers, developers, systems engineers, and domain experts; translates requirements into Jira stories and technical specifications; performs functional testing; evaluates AI architectures and model performance; and manages system-level integration tradeoffs through Agile delivery.

Own the technical post-sale customer journey for an agentic AI platform, including workflow analysis, implementation planning, data readiness, AI workflow configuration, enterprise integrations, deployment, security approvals, rollout, adoption, and expansion. Partner with customers, founders, Product, and R&D to deliver measurable business value and evolve the company’s solutions delivery function.

Lead an embedded AI squad from discovery to production: harden and deliver AI prototypes, design reusable agentic AI patterns and architectures, ensure security/data governance, integrate AI into enterprise systems, codify reference implementations, mentor engineers, and measure outcomes to scale solutions across the organization.