Top AI Engineer Jobs

Evaluates emerging AI technologies, platforms, infrastructure, and vendors through technical assessments, proof-of-concepts, benchmarks, and pilot deployments. Assesses solutions for capability, cost, risk, scalability, and business value; recommends adoption strategies; supports customers and internal stakeholders; delivers technical presentations, workshops, and demonstrations; and monitors developments in LLMs, automation, and agentic workflows.

Develop and support enterprise RPA solutions, including unattended UiPath robots, workflow automation, API and REST integrations, Office 365 automation, OCR, Document Understanding, and AI Fabric implementations. Responsibilities include technical design, development, robot monitoring and support, customer collaboration, process analysis, documentation, and applying RPA development best practices.

Develop AI-driven automation for government and national security workflows. Build and evaluate machine learning, LLM, NLP, data pipeline, and agentic systems; analyze complex datasets; create evaluation frameworks; and productionize reliable AI capabilities. Collaborate with engineers, mission specialists, and government users to translate ambiguous requirements into measurable solutions, demonstrate capabilities, gather feedback, and improve mission outcomes.

Build and deploy production-grade AI products end-to-end, from problem framing and data pipelines through live deployment and iteration. Collaborate directly with city leaders and customers, lead technical scoping workshops, and develop agentic solutions involving predictive modeling, real-time decision-making, data consolidation, and LLM-powered experiences. Design scalable APIs and reliable backend and frontend systems while helping establish the technical culture of a new AI Labs team.

Build and ship production-grade AI products end-to-end, from problem framing and data pipelines through deployment and iteration. Develop agentic solutions, predictive models, real-time decision-making tools, and LLM-powered experiences. Design scalable APIs and backend systems, collaborate with product and operations teams, lead technical scoping with city leaders, and help establish AI Labs’ engineering culture.

Develop and maintain full-stack web applications, including React-based interfaces, backend services, RESTful APIs, and Oracle PL/SQL solutions. Lead application analysis, design, development, testing, deployment, troubleshooting, security, and operational support. Collaborate with technology teams and business stakeholders, apply Agile practices, use CI/CD tools, leverage AI coding assistants, assess risks, and mentor junior analysts while operating with limited supervision.

Build the AI runtime for trustworthy agents: design orchestration, entity resolution, context assembly, connector orchestration, evidence retrieval, permissions and action policies. Own end-to-end features, prototype rapidly, implement evaluation frameworks, and collaborate with Product, Design, and Customer teams to deliver production-grade agent capabilities.

Lead technical discovery and solution design for enterprise AI initiatives. Build and deliver POCs for NLP, RAG, conversational AI, and integrations; own technical validation, RFP/RFI responses, and executive presentations. Develop light production code and integrations (Python plus Java/TypeScript), and manage Kubernetes/IaC/VPC deployments across AWS/Azure and on-prem environments. Frequent customer travel (20-40%).

Provide deep technical support for Devin and Windsurf by reproducing, diagnosing, and resolving complex customer issues across codebases, CI/CD, containers, cloud platforms, and developer tooling. Perform root-cause analysis, escalate with full technical context, create playbooks and tooling, and support release testing to improve reliability and adoption.

The AI Engineer will design, develop, and deploy machine learning models, work on APIs for AI applications, and manage ML pipelines on AWS. Responsibilities include productionizing AI systems and developing scalable solutions in a cloud-native environment.

Design, build, and support enterprise data platforms for analytics, reporting, and regulatory needs. Develop and optimize database objects and data models, automate data workflows with Python/Perl/Shell, provide Level 3 production support, improve data quality and performance, and leverage AI/LLM tools to enhance automation and analytics while embedding risk and control practices.

Lead and develop a solutions engineering team to design, demo, and deliver AI/ML solutions (LLMs, RAG, embeddings). Oversee pre-sales discovery, POCs, customer enablement, technical strategy, cross-functional alignment, escalation handling, hiring, and team enablement to drive revenue and customer success.

Designs, prototypes, implements, and supports AI and automation solutions across the university. Works with stakeholders to gather requirements, integrate systems, automate workflows, ensure security/privacy/FERPA compliance, and produce documentation, training, and post-implementation support to improve institutional operations.

Design, build, and operate AI-powered full-stack applications and agentic workflows on GCP. Own architecture, IaC, CI/CD, integrations, databases, and developer productivity tooling while automating repetitive work and scaling RAG/LLM systems for production.

Design, build, and operate Ford's cloud-native platform on GCP. Automate infrastructure and CI/CD, orchestrate AI-assisted agents, run and scale LLM/agent workloads, implement observability and SLO-driven reliability, and embed security and compliance into platform pipelines to enable fast, reliable delivery for product teams.

Design, build, and operate AI-powered full-stack applications and agentic workflows on GCP. Own architecture, IaC, CI/CD, integrations with enterprise SaaS, databases, and production-grade RAG/LLM pipelines. Automate developer workflows and ship scalable, secure systems.

Lead architecture, development, and production deployment of AI/ML solutions for cybersecurity, network analytics, and enterprise risk detection. Build and operationalize models (anomaly detection, predictive, classification), implement Generative/Agentic AI, design scalable ML pipelines, collaborate with security and IAM teams, and deliver dashboards and visualizations for stakeholders.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Support Regulatory Affairs by evaluating AI and emerging technologies, assisting with pilots and POCs, documenting requirements and process maps, contributing to process improvement, and ensuring responsible, compliant AI use while maintaining project documentation and collaborating across technical and business teams.