Top AI Engineer Jobs

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.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.

Lead a small AI engineering team to design, prototype, and deploy AI/LLM solutions for clinical workflows. Collaborate with Product and cross-functional teams, mentor junior engineers, and apply cutting-edge AI/ML research to production projects.

The Senior AI Engineer will design and develop voice agents and agentic workflows for healthcare operations, ensuring reliable interactions and efficient backend processes while monitoring performance and iterating on systems based on user feedback.

Design, implement, and operate LLM-powered and agentic AI features and shared infrastructure. Build RAG pipelines over financial data, integrate LLMs and tools into agentic workflows, and support production reliability, monitoring, and evaluation. Collaborate with product and design to translate customer needs into safe, compliant AI solutions.

Design, build, and operate production-grade agentic AI systems and LLM-powered features for financial workflows. Implement RAG pipelines over sensitive data, contribute to shared AI infrastructure (LLM services, orchestration, monitoring), and own reliability, security, and compliance in production while partnering with product and design.

The Applied AI Engineer will work at the intersection of product, engineering, and research to develop AI tools, influence engineering practices, and contribute to company culture while addressing customer challenges.

Build and operate production-grade agentic AI systems for cloud security and governance: design LLM-powered agents, RAG pipelines, integrate with cloud/SaaS security tools (SIEM, CNAPP/CSPM/SSPM), implement Security-as-Code/Policy-as-Code, contribute to shared AI infrastructure, and ensure secure, auditable AI operations.

Design, build, and deploy production-ready generative AI solutions (RAG, agentic workflows, MCP). Mentor engineers, partner across teams, create data pipelines and feature engineering, instrument observability, and optimize LLM performance to improve system reliability and incident response.

Build and ship production-grade agentic AI systems and LLM-powered features for financial workflows. Design RAG pipelines over sensitive data, contribute to shared AI infrastructure (LLM services, orchestration, monitoring), ensure reliability, security, and auditability, and collaborate with product and design while owning systems in production.