Top AI Engineer Jobs

The Senior AI Software Engineer will lead the development of a Risk Management Information System, enhancing claims management through AI and collaboration with various stakeholders. Responsibilities include defining technical roadmaps, providing architectural leadership, and delivering scalable solutions.

Lead rapid, AI-enabled prototyping from concept through launch. Use AI and modern game engines to build playable prototypes, design tools and agentic workflows, evaluate emerging AI tech, iterate with player feedback, and help define scalable AI development practices across the studio.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Design, build, and maintain automated test suites, evaluation scenarios, and CI/CD quality gates for AI-powered applications. Test APIs, validate ML model outputs, create measurable evaluation rubrics, mentor teams, and contribute to shared QA frameworks and codebases.

Design and build backend systems for AI workflows, optimize solutions using Python, integrate AI/ML tools, and collaborate with teams for enterprise applications.

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.

Embed with business units to prototype, deploy, and operationalize production-grade AI applications. Architect APIs, microservices, data pipelines and event-driven systems using Docker, Kubernetes, Kafka, and cloud platforms. Translate ambiguous business problems into measurable AI solutions, lead deployments (including shipboard constraints), mentor engineers, and feed field insights back to platform teams to improve reusable components and practices.

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.

Own end-to-end engineering of in-vehicle voice assistant features, driving architecture, integration, validation (HIL/SIL), and release. Collaborate with product, design, systems, audio/acoustics, and external partners to define requirements, manage interfaces, triage defects, and deliver OTA feature improvements across vehicle programs.

Design and build AI-powered agents, retrieval, ranking, and recommendation systems to manage enterprise ontology and metadata. Evaluate architectures, implement scalable backend services and APIs on cloud platforms, run proofs-of-concept, and collaborate with product and domain experts to deliver production AI solutions.

Lead Ford's strategic integration of AI across the Global Product Development System to optimize workflows, accelerate development, and enable digital-first engineering. Drive immediate AI pilots (LLM coding assistants, virtual program management), architect data systems and digital twins, scale predictive simulation, and champion cultural adoption. Oversee global, cross-functional transformation from tactical optimizations to an aspirational compressed, digitally driven development cycle.

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.