Top Machine Learning Jobs in Cleveland, OH

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads strategy, roadmap, discovery, launch, and optimization of AI-powered digital products across web, mobile, B2C, and B2B platforms. Partners with UX, engineering, data science, analytics, marketing, and vendors to define requirements, manage releases, establish KPIs, evaluate AI models, and ensure measurable customer and business value. Presents business cases, manages stakeholder alignment, and oversees model governance, performance monitoring, and continuous improvement.

Develops and industrializes AI-enabled engineering workflows across design, simulation, testing, and manufacturing. Responsibilities include building agentic AI optimization systems, integrating AI with engineering tools, developing multiphysics digital twins, creating physics-informed co-simulation engines, training CAD-aware transformer and graph neural network models, and deploying enterprise-scale digital threads.

Designs, develops, deploys, and operates scalable AI and machine learning solutions across the enterprise. Responsibilities include building end-to-end ML infrastructure, managing LLM lifecycles, optimizing RAG systems and prompts, evaluating models, deploying Azure-based solutions, writing tested software, supporting CI/CD and DevSecOps, monitoring production systems, and collaborating with engineering, architecture, DevOps, and testing teams.

Leads predictive planning and insights for industrial-business revenue growth. Develops forecasting models, machine learning-driven analytics, executive dashboards, scenario plans, and actionable commercial insights. Partners with Finance, Sales, Pricing, Marketing, and business leaders to improve forecast accuracy, identify revenue opportunities, integrate market signals, and modernize planning processes. Manages and develops a team while driving governance, automation, adoption, and continuous improvement across predictive analytics capabilities.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Leads application and AI security across the software development lifecycle, focusing on LLM-based, internally developed, and agentic AI applications. Partners with development and architecture teams on threat modeling, security requirements, secure design, vulnerability prioritization, remediation, and security standards. Reviews application architectures, source code, data flows, trust boundaries, and AI integrations while developing guidance, automation, training, and reusable security patterns.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads enterprise data architecture strategy, roadmap, and implementation across data models, pipelines, lakes, warehouses, and business intelligence. Establishes governance, quality, security, and compliance standards; evaluates emerging technologies; collaborates with business and IT stakeholders; mentors data professionals; resolves complex data challenges; and maintains architecture documentation.

The Senior Data Scientist develops quantitative models for financial crime compliance, performs data analysis, and collaborates across teams to enhance model performance.

Six-to-nine-month R&D co-op integrating data science and analytics into paint development. The role involves collaborating with chemists and scientists, completing technical data science projects, creating data visualizations, supporting digital initiatives, and presenting project outcomes. Participants also engage in performance reviews, networking, learning events, and cross-functional teamwork. The position is fully onsite in Brecksville, Ohio, and requires a background check.

Develop and deploy scalable AI applications and machine learning models using Python, Spark, Azure, and OpenAI. Responsibilities include requirements analysis, software development lifecycle participation, pipeline development, testing, deployment, troubleshooting, documentation, and production support. The role collaborates with data, software, and business teams to deliver data-driven solutions, while applying version control, CI/CD, and software engineering best practices.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Lead strategic sales of AI Workforce solutions, including Microsoft Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify opportunities, build pipeline, conduct consultative business and technical conversations, and orchestrate internal teams and partners to close complex deals. The role requires expertise in technology sales, AI, forecasting, demand generation, stakeholder engagement, competitive positioning, and cross-selling across enterprise and regulated customers.

Leads architecture and production delivery of a near-real-time observability platform for large-scale AI supercomputers. Responsibilities include telemetry ingestion, processing, storage, querying, topology and health modeling, 1P/3P data convergence, AI-assisted anomaly detection, technician dashboards, repair validation, security, reliability, cross-team technical leadership, and mentoring.

Conduct life insurance underwriting audits and complex case reviews, assess mortality and risk data, make decisions within an $8 million authority, and evaluate automated underwriting models. Partner with engineering, data science, actuarial, compliance, and customer experience teams to identify rule gaps, improve audit processes, and maintain underwriting accuracy as automated systems evolve.

Leads AI and machine learning engineering for federal and commercial digital transformation initiatives. Designs AI-enabled support models, develops prototypes, creates modernization roadmaps, advises executives, facilitates workshops, supports proposals, and builds business cases demonstrating automation ROI. The role requires hands-on expertise in conversational AI, virtual agents, agentic systems, workflow automation, LLM orchestration, safety guardrails, and AI evaluation, along with client-facing consulting, portfolio ownership, and the ability to obtain a Top Secret clearance.

Leads advanced analytics, AI, automation, and scientific computing initiatives supporting pharmaceutical clinical development. Translates scientific and business requirements into scalable analytical products, software specifications, decision-support tools, and production automation. Partners across biometrics, clinical development, IT, and business teams; evaluates emerging AI technologies; improves regulated workflows; establishes value metrics; drives adoption and change management; and mentors technical talent.