Top AI & Machine Learning Jobs

Manage enterprise AI platforms, including configuration, licensing, capacity, agent deployment, user onboarding, governance, security, compliance, monitoring, lifecycle management, and cost tracking. Partner with developers, architects, cybersecurity, legal, privacy, HR, and business teams to operate AI tools reliably. Establish standards for AI application lifecycle management, source control, deployment automation, release processes, access controls, and operational documentation.

Leads software developers and data scientists building automotive diagnostic and quality analytics products. Guides anomaly-detection modeling, generative AI and NLP adoption, ML pipeline development, Angular dashboard delivery, GCP deployment, MLOps, and production monitoring. Oversees priorities, resources, agile execution, engineering practices, and stakeholder communication while mentoring team members and translating vehicle-quality challenges into reliable data science and software solutions.

Leads a multidisciplinary team delivering advanced analytics, AI, and machine learning solutions across Ford. Provides hands-on technical leadership from concept through deployment, improves data quality and integration, partners with business stakeholders, mentors technical professionals, and develops technology adoption roadmaps. The role applies statistical analysis, optimization, simulation, machine learning, and AI to improve vehicle launch timing, quality, cost, manufacturing, supply chain, and enterprise decision-making.

Leads Ford’s strategic integration of AI, advanced analytics, digital twins, predictive simulations, and intelligent systems across the global vehicle product creation lifecycle. Responsibilities include identifying engineering bottlenecks, deploying AI tools, architecting connected data systems, scaling virtual validation, optimizing manufacturing tooling, and driving a digital-first development cycle. The role also establishes digital engineering practices and guides global, cross-functional teams through large-scale transformation.

Develop production-grade Python services, APIs, data models, analytical solutions, and machine learning pipelines. Build CI/CD and cloud automation on GCP, implement observability and safe deployment practices, and establish quality controls for AI-generated code. Partner with technical and business stakeholders to translate complex data into actionable insights, support AI-driven applications, and mentor others. The role requires strong data science, engineering, cloud, automation, and communication skills.

Build and operate AI-enabled capabilities for Ford Credit’s loan origination platform. Responsibilities include developing audited MCP tools, multi-agent workflows, hybrid graph and vector retrieval, deterministic Java/Kotlin backend services, evaluation systems, and AI observability. The role also diagnoses and enhances mainframe, Java/Spring, GCP, and Splunk-based systems, while authoring technical documentation and improving autonomous agent performance.

Design, develop, evaluate, and deploy NLP, LLM, generative AI, multimodal, and agentic applications. Responsibilities include fine-tuning models, conducting experiments, building scalable data and ML pipelines, developing APIs and user interfaces, and deploying applications with Docker, Kubernetes, and cloud infrastructure. The role also involves conversational agents, image-generation tools, performance monitoring, and production-ready AI products for automotive use cases.

Develop statistical, machine learning, and generative AI solutions across the full lifecycle, from problem definition and data acquisition through modeling, visualization, testing, deployment, and maintenance. Design agentic workflows, advanced RAG architectures, SLM strategies, orchestration frameworks, and evaluation benchmarks. Collaborate with product, design, engineering, and business teams to deliver actionable insights and communicate findings to technical and executive audiences.

Build scalable Python backend services, APIs, cloud-native applications, and AI-powered solutions. Develop LLM, RAG, agentic, and multi-agent systems; deploy workloads with Docker and Kubernetes; maintain CI/CD pipelines; and operate applications across major cloud platforms. Collaborate cross-functionally to deliver secure, tested, observable, and production-ready software while optimizing performance, scalability, latency, and cost.

Develop and deploy AI and analytics models for battery manufacturing quality, defect detection, yield, scrap, and throughput improvement. Analyze plant data from MES, historians, PLCs, OT systems, and inline metrology. Partner with process, controls, quality, operators, vendors, and central technologists to convert model insights into process changes, error-proofing, and standard work while sharing reusable improvements across plants.