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Privacy Preserving Machine Learning Jobs in Cleveland, OH

Flexjet LLC

Director of AI Engineering

Cleveland, OH · On-site

$180 - $260/hr

Oversee the production deployment of machine learning and LLM‑powered applications, including RAG ... Ensure compliance with responsible AI, security, risk management, data privacy, auditability ...

Flexjet

Senior Data Scientist

Cleveland, OH · On-site

Responsibilities : • Design and implement enterprise-scale machine learning models, including ... governance, privacy, and security standards • Support model explainability and documentation ...

Flexjet

Director of AI Engineering

Cleveland, OH

... scale machine learning and generative AI systems. This role is responsible for building and ... privacy, auditability, reproducibility, documentation, and regulatory requirements. · Build and ...

Incedo Inc

Software Architect - Java

Strongsville, OH · On-site

$58.25 - $78.50/hr

Machine learning solution design and integration * Model-serving and API-integration patterns * Data security, governance, and privacy * Identification of practical machine learning use cases that ...

Accenture

Responsible AI Engineer

Cleveland, OH · On-site

... Privacy - preferably gained in a client-facing environment. You'll be a problem solver who is ... Machine Learning Visualization Tools * Minimum of 3 years of experience in the following:

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All JobsPrivacy Preserving Machine Learning JobsPrivacy Preserving Machine Learning Jobs in Cleveland, OH

Privacy Preserving Machine Learning information

See Cleveland, OH salary details

$96.5K

$112K

$125.6K

How much do privacy preserving machine learning jobs pay per year?

As of Sep 2, 2026, the average yearly pay for privacy preserving machine learning in Cleveland, OH is $112,020.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $125,100.00 per year, depending on experience, location, and employer.

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 Cleveland, OH?

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

What job categories do people searching Privacy Preserving Machine Learning jobs in Cleveland, OH look for?

The top searched job categories for Privacy Preserving Machine Learning jobs in Cleveland, OH are:

What cities near Cleveland, OH are hiring for Privacy Preserving Machine Learning jobs?

Cities near Cleveland, OH with the most Privacy Preserving Machine Learning job openings:

Privacy Preserving Machine Learning jobs near you

Director of AI Engineering

Flexjet LLC

Cleveland, OH • On-site

$180 - $260/hr

Other

Re-posted 27 days ago

Flexjet rating

8.2

Company rating: 8.2 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

19th of 67 rated aviation services

Job description

Current job opportunities are posted here as they become available.

Flexjet is seeking a Director of AI Engineering to lead the design, deployment, and operationalization of enterprise‑scale machine learning and generative AI systems. This role is responsible for building and managing the infrastructure, systems, and processes required to reliably deploy and maintain AI solutions in production. Combine strong engineering leadership with deep expertise in MLOps, cloud infrastructure, model lifecycle management, and Generative AI deployment. Lead a team of AI engineers and MLOps specialists to ensure scalable, secure, and compliant AI systems across the organization.

DUTIES & RESPONSIBILITIES
  • Lead the strategy, architecture, and implementation of enterprise AI, Generative AI, and MLOps platforms while establishing standards for model development, deployment, monitoring, governance, and lifecycle management.
  • Design and scale cloud‑native AI infrastructure, including distributed compute environments, containerized platforms, CI/CD pipelines, and cost‑optimized ML operations.
  • Oversee the production deployment of machine learning and LLM‑powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated retraining processes.
  • Ensure compliance with responsible AI, security, risk management, data privacy, auditability, reproducibility, documentation, and regulatory requirements.
  • Build and manage reusable AI platform services and frameworks that support multiple data science and engineering teams.
  • Lead, mentor, and grow teams of AI Engineers and MLOps Engineers, fostering engineering excellence, innovation, talent development, and performance accountability.
  • Partner with Data Scientists, Software Engineering, Security, DevOps, and Product leadership teams to drive enterprise AI adoption and align technical strategy with business objectives.
  • Communicate AI platform vision, roadmap, and operational performance to executive stakeholders.
EDUCATION & EXPERIENCE
  • Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field, or an equivalent combination of education, training, and relevant professional experience.
  • 10+ years of experience in software engineering, machine learning engineering, platform engineering, MLOps, or DevOps.
  • 5+ years of leadership experience managing and mentoring technical teams in fast‑paced, technology‑driven environments.
  • Experience implementing and deploying complex and integrated information systems.
  • Proven experience in leading application development teams in an enterprise environment.
  • Experience working with Agile methodology.
  • Experience in managing large projects including setting deadlines, identifying interdependencies, communicating with stakeholders, gathering requirements, and setting expectations.
REQUIRED TECHNICAL SKILLS & QUALIFICATIONS
  • Strong experience with MLOps and platform engineering, including model lifecycle management, CI/CD, model versioning, feature stores, experiment tracking, and automated retraining pipelines.
  • Proficiency with cloud and infrastructure technologies, including AWS, Azure, or Google Cloud Platform (GCP), Kubernetes, Docker, Terraform, and distributed systems.
  • Expertise in machine learning systems, including model deployment, monitoring and observability, data pipelines, and real‑time inference architectures.
  • Experience with Generative AI and LLM technologies, including LLM deployment, Retrieval‑Augmented Generation (RAG), prompt orchestration, model governance and guardrails, and cost optimization strategies.
  • Strong programming skills in Python, SQL, Bash, Git, and CI/CD tools.
PREFFERED QUALIFICATIONS
  • Experience deploying Generative AI and LLM solutions in large‑scale enterprise environments.
  • Experience designing and supporting multi‑tenant AI/ML platforms.
  • Familiarity with RAG architectures, vector databases, and LLM evaluation frameworks.
  • Experience managing GPU infrastructure and distributed training workloads.
  • Knowledge of AI security, governance, risk management, and regulatory compliance frameworks.
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