Top AI Engineer Jobs

Develop, train, evaluate, optimize, and deploy machine learning models and AI applications using Python and PyTorch. Prepare datasets, build REST APIs for model serving, troubleshoot data and API issues, and maintain model versions and documentation. Collaborate with engineering, data science, and product teams while following software development practices such as Git, code reviews, automated testing, and deployment workflows.

Develop and deploy AI-powered applications, integrating large language models, APIs, chatbots, RAG, embeddings, and vector databases. Build backend services, optimize application quality and performance, implement security and responsible AI practices, and monitor accuracy, latency, reliability, and costs. Collaborate with product, design, and engineering teams while supporting deployment and documentation.

Build AI-powered features and scalable agent infrastructure for Hex’s data analytics platform. Partner with research and product engineering teams on context, model prompting, experimentation tooling, and platform integration. Lead major product initiatives, develop performant web experiences, maintain high standards for testing and correctness, and mentor engineers. The role involves full-stack development across React, TypeScript, GraphQL, backend services, cloud infrastructure, and data systems.

Build and deploy AI capabilities for Hex’s Notebook Agent and context engine. Responsibilities include running experiments, fine-tuning models, developing AI infrastructure and experimentation tooling, architecting AI-enabled systems, and shipping production experiences for data scientists and analysts. The role partners closely with product teams, spans greenfield initiatives, and offers opportunities for technical and team leadership.

Build and deploy AI-powered automation workflows for marketing, growth, content, sales collateral, social distribution, and community operations. Responsibilities include auditing existing processes, automating blog production and publishing, encoding brand voice, integrating platform APIs, implementing human approval steps, documenting workflows, and handing them off to the team.

Lead technical pre-sales for enterprise AI applications: run demos, PoCs, hackathons, and architecture workshops. Design secure, scalable cloud-native AI solutions, resolve technical blockers, and advise customers on AI Foundry, Azure AI, Responsible AI, and AI management/security to accelerate adoption and deployment.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Build, productionize, and operate AI-driven features and integrations across frontend, backend, and model layers. Prototype and deliver agent-driven experiences (conversational interfaces, RAG, tool-calling), integrate LLMs and external data sources, and collaborate with product and design. Participate in on-call/standby duties.

Designs and implements AI-enabled enterprise solutions across Oracle Cloud HCM, SCM, Financials, Procurement, and analytics platforms. Develops predictive analytics, intelligent workflows, digital assistants, anomaly detection, automation, dashboards, and reporting. Partners with business and technical teams on process analysis, integrations, data governance, testing, upgrades, and continuous improvement. Evaluates Oracle AI capabilities and quarterly releases, supports adoption, troubleshoots systems, and provides documentation, training, and user support.

Supports senior engineers developing AI-driven image inspection systems for manufacturing. Responsibilities include image collection, software implementation, testing, algorithm evaluation, documentation, and integration of 2D imaging, 3D laser scanning, Visual AI, machine learning, computer vision, and automation technologies. The intern manages multiple projects, collaborates cross-functionally, and contributes to improving inspection accuracy, reliability, and operational efficiency in a regulated manufacturing environment.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows for enterprise customers. Own system architecture from proposal through deployment, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement continuous deployment, monitoring, logging, security, and reliability practices while leveraging cloud platforms, containers, vector databases, and modern AI development tools. Mentor engineers and take ownership of resilient production code.

Partner with enterprise customers to identify AI use cases and deliver tailored applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI capabilities into practical recommendations, collaborate with Product and Engineering, improve technical implementation processes, and drive customer adoption, expansion, and measurable business outcomes.

Partner with enterprise customers to identify AI use cases, architect and implement tailored applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into actionable recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and measurable business outcomes.

Partner with Fortune 500 customers to identify AI use cases, architect and implement custom applications, templates, and integrations, and guide enterprise adoption. The role uses Python, APIs, generative AI, and Knowledge Graph capabilities while translating complex technical concepts for business and executive audiences. It also develops scalable integration processes, documentation, and workflows; provides customer feedback to Product and Engineering; and drives successful outcomes and expansion within strategic accounts.

Lead the architecture, development, deployment, and operation of production-grade AI platforms and applications. Build scalable cloud-native services integrating generative AI, LLMs, intelligent document processing, APIs, microservices, data pipelines, and evaluation frameworks. Provide hands-on technical leadership, mentor engineers and data scientists, establish engineering standards, and ensure AI systems are secure, reliable, observable, compliant, and cost-efficient.

Build and operate enterprise AI platforms across Azure and GCP, including Kubernetes clusters, Terraform infrastructure, CI/CD and GitOps pipelines, GPU workloads, model gateways, vector databases, tracing, and evaluation services. Develop reference agents and LLM application templates, support application teams integrating workloads, troubleshoot platform reliability, automate operations, and create runbooks. The role requires Chicago office attendance three days weekly and up to 20% domestic travel.

Operate and improve a self-hosted AI platform across customer Kubernetes environments. Responsibilities include Helm and GitOps deployments, stateful service reliability, incident response, SLOs, capacity planning, Terraform infrastructure across major clouds, CI/CD automation, security controls, troubleshooting, and customer support. The role also develops runbooks, documentation, and automation while supporting private, air-gapped, and regulated environments.

Lead the architecture and delivery of an enterprise AI platform supporting LLM and agentic workloads. Design multi-tenant Kubernetes infrastructure across Azure and GCP, GPU capacity, Terraform modules, CI/CD, GitOps, security controls, model-serving services, and governance. Mentor a platform engineer, lead architecture reviews, advise client stakeholders, and establish operational standards for production AI infrastructure.

Lead the architecture and delivery of an enterprise AI platform supporting LLM and agent workloads. Design multi-tenant Kubernetes infrastructure across Azure and GCP, GPU capacity, Terraform modules, CI/CD and GitOps workflows, security controls, governance, and shared AI services. Mentor a platform engineer, guide technical decisions, conduct architecture reviews, and advise client stakeholders.

Build and operate Databricks-based batch and streaming pipelines for analytics and enterprise AI workloads. Design lakehouse data models, governance, access controls, quality monitoring, and reliable cloud infrastructure. Develop ingestion and chunking pipelines for unstructured documents, embeddings, vector search, and LLM evaluation datasets supporting RAG systems. Collaborate with AI and platform engineers while applying software engineering practices such as version control, testing, and CI/CD.

Build and operate an enterprise AI platform across Azure and GCP, including Kubernetes clusters, Terraform modules, CI/CD and GitOps pipelines. Deploy and monitor LLM platform services such as model gateways, vector databases, tracing, and evaluation systems. Support GPU workloads, automate operations, create reference agents and LLM application templates, and help application teams integrate and troubleshoot production workloads.

Build and maintain Databricks-based batch and streaming data pipelines for an enterprise AI platform. Responsibilities include lakehouse modeling, governance, data quality, unstructured document ingestion, chunking, embeddings, vector search, and LLM evaluation datasets. The role deploys cloud infrastructure on Azure or GCP, applies testing and CI/CD practices, and partners with AI and platform engineers to deliver secure, reliable data for retrieval-augmented generation systems.

Operate and improve a self-hosted AI platform across customer Kubernetes environments. Responsibilities include deployments, upgrades, stateful service reliability, incident response, SLOs, capacity planning, Terraform infrastructure, CI/CD, security controls, automation, troubleshooting, and customer support. The role also develops Helm charts, runbooks, and documentation while supporting private, air-gapped, and regulated environments across AWS, GCP, and Azure.

Develop and validate AI-assisted power system study workflows for generation and large-load interconnection. Automate model preparation, simulations, contingency and power-flow analysis, data checks, results extraction, and reporting using Python and power-system tools. Validate automated outputs against engineering expectations, planning criteria, reference cases, and manual studies. Collaborate with software, AI, product, and power systems teams while documenting assumptions, methods, limitations, and results.

Develop and improve machine learning models for robotic manipulation, including VLA and world-action models. Build robot-learning data pipelines, conduct simulation and real-robot evaluations, and deploy optimized models for real-time onboard inference. The role involves multimodal learning, reinforcement learning, tactile and force-torque sensing, and close collaboration with robotics, hardware, and product teams. This is a full-time, 3–6 month internship requiring five days per week in the office.