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Privacy Preserving Machine Learning Jobs in Ohio

Incedo Inc

Senior Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

... transparency, privacy, security, and compliance. * Perform model risk evaluations , complete ... Evaluate emerging machine learning and AI technologies and recommend appropriate adoption ...

Sabio infotech

Sr. Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

... machine learning and Generative AI solutions with a focus on reliability, performance, security ... transparency, privacy, security, and compliance. · Perform model risk evaluations, complete ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Wells Fargo

Senior Specialty Protocol Engineer

Columbus, OH · On-site

$100K - $138K/yr

Implement and optimize privacy-preserving primitives, specifically Pedersen Commitments and ... State Machine Design: Design a customKVStorestate machine that manages homomorphic encrypted ...

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Privacy Preserving Machine Learning information

What is privacy preserving machine learning?

Privacy preserving machine learning refers to techniques and methods that allow data analysis and model training while protecting sensitive information. This field focuses on ensuring that personal or confidential data is not exposed or compromised during the development and deployment of machine learning models. Approaches such as federated learning, differential privacy, and homomorphic encryption are commonly used. These methods enable organizations to leverage data for insights and predictions without violating privacy regulations or risking data breaches. Privacy preserving machine learning is especially important in industries like healthcare, finance, and any sector handling personal data.

What are the key skills and qualifications needed to thrive as a privacy preserving machine learning engineer?

To thrive as a Privacy Preserving Machine Learning Engineer, you need a strong background in machine learning, data privacy techniques (such as differential privacy or federated learning), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow Privacy, PySyft, and privacy-enhancing technologies, along with certifications in data security or privacy, are often required. Strong problem-solving abilities, meticulous attention to detail, and the ability to communicate complex technical concepts clearly set top professionals apart. These skills ensure the development of robust machine learning models that protect sensitive data while delivering valuable insights, maintaining compliance and trust.

What are some common challenges faced by professionals working in privacy preserving machine learning roles?

Professionals in Privacy Preserving Machine Learning often encounter challenges such as balancing model accuracy with strict privacy requirements, selecting appropriate privacy-preserving techniques (like differential privacy or federated learning), and ensuring compliance with evolving data protection regulations. Collaborative projects may also involve coordinating with legal, data security, and software engineering teams to implement robust solutions. Additionally, staying updated with the latest research and adapting to new threats or vulnerabilities is a continuous part of the role.

What is the difference between Privacy Preserving Machine Learning vs Data Scientist?

AspectPrivacy Preserving Machine LearningData Scientist
Required CredentialsTypically requires knowledge of machine learning, data privacy, and security certificationsRequires degrees in data science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentWorks in research, development, and implementation of privacy-focused ML models, often in tech or finance sectorsAnalyzes data, builds models, and provides insights across various industries including marketing, finance, and healthcare
Employer & Industry UsageUsed by organizations prioritizing data privacy, such as healthcare, finance, and tech companiesEmployed across diverse sectors for data analysis, predictive modeling, and decision support

Privacy Preserving Machine Learning focuses on developing models that protect data privacy during training and inference, while Data Scientists analyze and interpret data to generate insights. Both roles require strong analytical skills, but Privacy Preserving Machine Learning emphasizes security and privacy techniques, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Privacy Preserving Machine Learning jobs in Ohio?

For Privacy Preserving Machine Learning jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Privacy Preserving Machine Learning jobs in Ohio look for?

The top searched job categories for Privacy Preserving Machine Learning jobs in Ohio are:

What cities in Ohio are hiring for Privacy Preserving Machine Learning jobs?

Cities in Ohio with the most Privacy Preserving Machine Learning job openings:

Privacy Preserving Machine Learning jobs near you

Senior Machine Learning Engineer

Cincinnati, OH • On-site

TUPPL Technology Inc
See more jobs at TUPPL Technology Incexternal

$100K - $137K/yr

Other

Posted 5 days ago

Job description

Role : Senior Machine Learning Engineer

Location: Cincinnati OH

Full-Time 

We are seeking an experienced Machine Learning Engineer with a strong bias for action, an ownership mindset, and a passion for solving complex business problems through automation and AI. The ideal candidate demonstrates technical excellence, leads by example, and has proven experience delivering enterprise-grade machine learning and Generative AI solutions in regulated environments.

Key Responsibilities

  • Design, develop, deploy, and maintain scalable machine learning and Generative AI solutions with a focus on reliability, performance, security, and business value.
  • Champion an automation-first approach to software and AI engineering, identifying opportunities to improve operational efficiency and reduce manual processes.
  • Build and operationalize machine learning models and AI-enabled applications throughout the entire model lifecycle, from experimentation to production deployment and monitoring.
  • Develop and deploy Generative AI applications in production environments, preferably within financial services or other highly regulated industries.
  • Apply and advocate Responsible AI principles, ensuring solutions meet requirements for fairness, explainability, transparency, privacy, security, and compliance.
  • Perform model risk evaluations, complete required governance documentation and questionnaires, and partner with stakeholders to address and remediate identified risks.
  • Establish and maintain frameworks for MLOps, model lifecycle management, monitoring, validation, version control, auditability, and AI governance.
  • Collaborate with Risk, Compliance, Information Security, and business partners to ensure machine learning solutions meet enterprise and regulatory standards.
  • Implement CI/CD pipelines, automated testing, model monitoring, observability, and production support processes for machine learning applications.
  • Evaluate emerging machine learning and AI technologies and recommend appropriate adoption strategies.
  • Mentor team members on best practices in machine learning engineering, MLOps, Responsible AI, and production AI systems.

Required Qualifications

  • Extensive experience designing, developing, and deploying machine learning solutions in production environments.
  • Hands-on experience developing and deploying Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and modern AI development frameworks.
  • Strong understanding of machine learning model development, feature engineering, model evaluation, performance optimization, and model monitoring.
  • Experience conducting model risk assessments and supporting governance, compliance, and validation requirements within regulated environments.
  • Practical experience implementing MLOps practices including model deployment, versioning, monitoring, automated retraining, and CI/CD pipelines.
  • Strong understanding of Responsible AI, model explainability, governance, and risk management concepts.
  • Proficiency in Python and modern machine learning ecosystems, including frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, Semantic Kernel, or equivalent technologies.
  • Strong communication, problem-solving, and stakeholder management skills.

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