AI Engineer

• Own the enterprise AI Governance architecture roadmap and technical implementation for OneTrust and associated GRC platforms.
• Define and lead enterprise architecture for AI Governance and Cyber GRC workflows.
• Own end-to-end AI governance lifecycle design: intake, classification, risk tiering, review/approval gates, exception handling, monitoring, and periodic reassessment.
• Translate policy and regulatory obligations into technical control patterns and reusable architecture blueprints deployable across UAT and production.
• Design and govern resilient API integrations across OneTrust, Archer, and broader enterprise systems.
• Establish role, group, and organizational segmentation architecture to enforce least-privilege access and scalable governance operations.
• Lead architecture decisions for automation, error handling, observability, incident response, and production support readiness.
• Chair cross-functional architecture reviews and influence risk, compliance, and engineering decision forums on control design and prioritization.
• Mentor engineers and analysts through architecture standards, implementation playbooks, and design quality reviews.

AI GRC Architect

• Lead and mentor teams of data scientists and ML engineers
• Manage client relationships and promote satisfaction with deliverables
• Translate intricate business challenges into AI-driven strategies
• Design and implement AI solution architectures
• Drive innovation and excellence in client engagements
• Analyze data to derive actionable insights and solutions
• Collaborate with stakeholders to align on project objectives
• Uphold exceptional standards of quality and integrity in every task

ERP AI Engineer – Manager

• Analyzing client needs to implement and integrate business applications effectively
• Providing training and support to clients for seamless utilization of software solutions
• Leveraging analytical reasoning to optimize operational efficiency through business applications
• Collaborating with clients to understand and address their strategic objectives
• Utilizing business data analytics to inform insights and recommendations
• Managing project delivery and resource management to meet client expectations
• Implementing technology solutions to support business transformation initiatives
• Developing documentation to support application software implementation and integration
• Engaging in strategic planning to align technology solutions with business goals
• Navigating complex situations to build meaningful client relationships and inspire team members

AI Integration Developer, Senior Associate

• Architect and implement artificial intelligence and machine learning systems addressing diverse business challenges throughout the Bank
• Conduct rigorous data analysis and perform statistical evaluation
• Design experimental frameworks and develop algorithms leveraging both structured and unstructured data
• Create on-demand analytics to fully integrated software systems
• Stay apprised of and educate leaders on emerging AI trends, risks, and opportunities
• Create and maintain model documentation for transparency
• Research, test, and apply state-of-the-art generative AI models and/or solutions
• Develop agentic AI systems capable of autonomous reasoning, planning, and tool utilization
• Create evaluation frameworks to assess model performance
• Translate business questions into analytical problems with clear objectives
• Lead projects with limited supervision, influencing technical direction and standards
• Partner with cross-functional teams to understand business requirements and translate into technical solutions

Principal AI Engineer – AI Transformation

• Develop and maintain relationships with homebuyers, Realtors, builders, financial advisors, and referral partners.
• Guide borrowers through the mortgage process from application through closing.
• Analyze financial information and recommend appropriate loan solutions.
• Build and manage a consistent referral pipeline through networking and relationship development.
• Deliver exceptional customer service throughout the lending process.
• Maintain compliance with all federal, state, and company lending guidelines.
• Grow a sustainable book of business through outstanding client experiences and referral relationships.

Licensed Mortgage Loan Originator

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

Machine Learning Engineer

• Design, implement, and evaluate RL algorithms for robotic control, motion planning, and adaptive behaviors in dynamic, unstructured environments.
• Develop and integrate RL policies with robot control systems, ensuring compatibility with hardware constraints and real-time requirements.
• Collaborate with perception teams to fuse RL with vision, depth, and sensor data for robust decision-making.
• Build and maintain sim-to-real pipelines, including domain randomization and transfer learning techniques.
• Conduct experiments on physical robots, including designing safety protocols and monitoring for unexpected behaviors.
• Leverage simulation environments (Isaac Gym, Gazebo, MuJoCo, PyBullet) for large-scale training before real-world validation.
• Continuously improve model efficiency to operate within compute and latency constraints on embedded robotic systems.

Senior Machine Learning Engineer – Reinforcement Learning

• Provide governance, oversight, and controlled expansion of internal AI tool
• Execute quarterly, risk-based testing program for independent credible challenge
• Collaborate with various stakeholders to review AI initiatives and escalate risks
• Evaluate controls across the AI lifecycle
• Translate regulatory requirements into AI-focused testing procedures
• Develop credible challenge documentation and reporting for governance committees
• Enhance AI testing frameworks and tools

Lead AI Compliance Testing

• Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost
• Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
• Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
• Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
• Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
• Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
• Leverage AI-augmented development tools to increase development velocity and code quality

Staff Machine Learning Engineer

• Shipping, evaluating, securing, and supporting real systems
• Development on agentic AI engineering technology
• Daily or weekly use of AI coding tools
• Model evaluation and quality measurement
• Maintain active relationships with customers to determine business requirements
• Owns the change request process and coordinates with other teams
• Develop and define application scope and objectives
• Performs technical design reviews and code reviews
• Coordinates and manages on-call support and owns the system monitoring process
• Leads multiple or large projects

Senior AI-Native Software Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• The Principal AI Engineer architects and implements artificial intelligence and machine learning systems that address diverse business challenges throughout the Bank, with a strong focus on research and experimentation of generative AI models and agentic systems.
• This role involves conducting rigorous data analysis, performing statistical evaluation, designing experimental frameworks, and developing algorithms that effectively leverage both structured and unstructured data.
• The AI Engineer creates solutions that range from on-demand analytics to fully integrated software systems, working closely with cross-functional teams to align technical solutions with business requirements.
• Staying apprised and educating senior leaders on emerging AI trends, risks, and opportunities.
• Responsible and accountable for risk by openly exchanging ideas and opinions, elevating concerns, and personally following policies and procedures as defined.
• Accountable for always doing the right thing for customers and colleagues and ensures that actions and behaviors drive a positive customer experience.
• While operating within the Bank's risk appetite, achieves results by consistently identifying, assessing, managing, monitoring, and reporting risks of all types.

• Apply automated testing and governance controls effectively
• Analyze complex issues and provide actionable insights
• Mentor and guide junior associates in their tasks
• Uphold rigorous standards of quality and technical excellence

Senior Associate – AI Engineering

• Manage a focused portfolio of strategic customer co-innovation accounts.
• Support top-to-top meetings, executive briefings, and customer workshops focused on AI, autonomy, and emerging technologies.
• Present Rockwell's co-innovation model and help customers understand how to move from interest to structured engagement.
• Partner with account teams to define customer goals, commercial relevance, and next steps.
• Define and operationalize a scalable co-innovation framework, including governance cadence across sales, R&D, and services stakeholders.
• Track progress, milestones, and outcomes across the co-innovation portfolio; connect engagements to pipeline and revenue impact.
• Facilitate regular updates, decision points, and escalation paths – maintaining commercial alignment and keeping projects properly scoped against agreed objectives.
• Connect co-innovation activity to pipeline, revenue potential, customer proof points, and repeatable use cases.
• Capture insights from customer engagements and share structured feedback with product, R&D, and commercial teams.
• Identify patterns across accounts that can inform future offerings, messaging, or productization opportunities.
• Turn co-innovation outcomes into content for external events, including Automation Fair and customer showcases.
• Partner with marketing, enablement, and commercial teams to package successful engagements into field-ready examples.
• Bring a growth mindset, a bias toward inclusion, and a commitment to integrity and accountability to every program and every team you touch.

Customer Co-Innovation Lead – AI & Autonomy

• Leveraging advanced analytics and statistical techniques to extract insights from large datasets
• Designing and developing robust data solutions to transform raw data into actionable insights
• Applying data science algorithms and machine learning techniques to solve complex business problems
• Building data pipelines and confirming data quality for informed decision-making
• Conducting exploratory and descriptive analysis to inform strategic decisions
• Developing predictive models using programming languages such as R and MATLAB
• Utilizing TensorFlow and Scikit-Learn for deep learning and natural language processing tasks
• Collaborating with team members to adapt to varying client challenges and scopes
• Taking ownership of projects and consistently delivering quality work that drives client value
• Engaging in active listening and communication to appreciate diverse perspectives and needs
• Upholding professional and technical standards while adhering to the Firm's code of conduct

CTIO, AI Engineer

LLM Engineer

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI Engineer

• Lead and coordinate the technical and business discussions on future AI architectural direction
• Analyze, design, and develop a roadmap for AI enabled engineering capabilities
• Review, analyze, and develop architecture at domain level and across multiple teams
• Participate in the enterprise architecture domain governance model
• Lead the research and evaluation of emerging AI technology
• Set model and agent lifecycle practice
• Provide leadership, technology guidance, and mentorship throughout the domain
• Develop peer, cross functional, and cross business relationships
• Lead the AI Engineering team, with the ability to attract, develop, and retain talent

Senior Director, AI Architecture

• Design and implement advanced AI and machine learning solutions
• Analyze intricate challenges and provide actionable insights
• Mentor and guide junior team members in their professional development
• Cultivate meaningful relationships with clients to understand their needs
• Navigate the rapidly changing technology landscape effectively
• Enhance personal knowledge and contribute to team insights
• Collaborate with cross-functional teams to deliver exceptional outcomes
• Uphold professional standards and ethical practices in every engagement

Senior ERP AI Engineer

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Accountable for deployment design, development and maintenance of both traditional ML and AI models.
• Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
• Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.
• Adopt and promote MLOps best practices to the Data Science community.

Machine Learning Engineer jobs in Ohio

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AI GRC Architect

ERP AI Engineer – Manager

AI Integration Developer, Senior Associate

Principal AI Engineer – AI Transformation

Licensed Mortgage Loan Originator

Product Engineer – AI

Senior Machine Learning Engineer – Reinforcement Learning

Lead AI Compliance Testing

Staff Machine Learning Engineer

Senior AI-Native Software Engineer

AI Engineer

Enterprise AI Architect

Principal AI Engineer – AI Transformation

Senior Associate – AI Engineering

Customer Co-Innovation Lead – AI & Autonomy

CTIO, AI Engineer

Senior MLOps, Generative AI Engineer

Senior Director, AI Architecture

Senior ERP AI Engineer

AI Machine Learning Engineer