AI Data Engineer - Manager
Cincinnati, OH · On-site
... machine learning models and large language models. • Conduct research to provide technical ... adversarial samples. • Help AI product managers and business stakeholders understand the ...
AI Data Engineer - Manager
Cincinnati, OH · On-site
... machine learning models and large language models. • Conduct research to provide technical ... adversarial samples. • Help AI product managers and business stakeholders understand the ...
AI Data Engineer - Manager
Columbus, OH · On-site
... machine learning models and large language models. • Conduct research to provide technical ... adversarial samples. • Help AI product managers and business stakeholders understand the ...
AI Data Engineer - Manager
Columbus, OH · On-site
... machine learning models and large language models. • Conduct research to provide technical ... adversarial samples. • Help AI product managers and business stakeholders understand the ...
AI Data Engineer - Manager
$109.90K - $131.90K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
AI Data Engineer - Manager
$109.90K - $131.90K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
AI Data Engineer - Manager
$110.60K - $132.80K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
AI Data Engineer - Manager
$110.60K - $132.80K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
AI Data Engineer - Manager
Cleveland, OH · On-site
$111K - $133.40K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
AI Data Engineer - Manager
Cleveland, OH · On-site
$111K - $133.40K/yr
Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
Staff AI Software Engineer
Beavercreek, OH · On-site
$194.70K - $292.10K/yr
Apply machine learning algorithms to large sets of structured and unstructured data to solve a ... Familiarity with explainable AI, adversarial AI, responsible AI (NIST AI Risk Framework)
Staff AI Software Engineer
Beavercreek, OH · On-site
$194.70K - $292.10K/yr
Apply machine learning algorithms to large sets of structured and unstructured data to solve a ... Familiarity with explainable AI, adversarial AI, responsible AI (NIST AI Risk Framework)
Staff AI Software Engineer
Beavercreek, OH · On-site
$194.70K - $292.10K/yr
Apply machine learning algorithms to large sets of structured and unstructured data to solve a ... Familiarity with explainable AI, adversarial AI, responsible AI (NIST AI Risk Framework)
Staff AI Software Engineer
Beavercreek, OH · On-site
$194.70K - $292.10K/yr
Apply machine learning algorithms to large sets of structured and unstructured data to solve a ... Familiarity with explainable AI, adversarial AI, responsible AI (NIST AI Risk Framework)
Adversarial Machine Learning information
What are the key skills and qualifications needed to thrive as an Adversarial Machine Learning specialist, and why are they important?
To excel in Adversarial Machine Learning, you need a strong background in machine learning, deep learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with adversarial attack and defense libraries, and knowledge of security protocols are crucial. Creative problem-solving, critical thinking, and strong communication skills help in designing robust models and explaining complex threats to stakeholders. These competencies are vital to anticipate vulnerabilities, safeguard AI systems, and ensure the reliability of machine learning models in real-world applications.
What are some common challenges faced by professionals working in Adversarial Machine Learning roles?
Adversarial Machine Learning professionals often face the challenge of staying ahead of rapidly evolving attack techniques that can compromise model integrity and security. Managing the balance between model performance and robustness is another key difficulty, as defenses against adversarial attacks can sometimes reduce accuracy or increase computational costs. Collaboration with data scientists, security teams, and software engineers is vital for developing resilient models and implementing effective defenses. Staying current with the latest research and tools is essential for success in this dynamic field.
What is adversarial machine learning?
Adversarial machine learning is a field of study focused on understanding and defending against attacks that manipulate machine learning models by feeding them deceptive input, known as adversarial examples. These attacks can cause models to make incorrect predictions, raising concerns about the security and reliability of AI systems, especially in critical applications like image recognition and autonomous vehicles. Researchers in this area develop techniques to detect, prevent, and mitigate these vulnerabilities to make machine learning systems more robust.
What is the difference between Adversarial Machine Learning vs Data Scientist?
| Aspect | Adversarial Machine Learning | Data Scientist |
|---|---|---|
| Credentials | Knowledge of machine learning, cybersecurity, and threat detection | Degree in data science, statistics, or related fields |
| Work Environment | Research labs, cybersecurity teams, AI development | Business analytics, data analysis, model development |
| Industry Usage | AI security, cybersecurity, machine learning research | Business, finance, healthcare, tech companies |
Adversarial Machine Learning focuses on understanding and defending AI models against malicious inputs, often within cybersecurity contexts. Data Scientists analyze data to extract insights, build models, and support decision-making across various industries. While both roles require machine learning knowledge, Adversarial Machine Learning emphasizes security and robustness, whereas Data Scientists focus on data analysis and predictive modeling.
What are popular job titles related to Adversarial Machine Learning jobs in Ohio? For Adversarial Machine Learning jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in Ohio look for? The top searched job categories for Adversarial Machine Learning jobs in Ohio are:
- Machine Vision Software Engineer
- Mlops Machine Learning Engineer
- Ai Prompt Engineer Internship
- Contract Apple Machine Learning Engineer
- Machine Learning Engineer Quantization
- Machine Learning Engineer Hybrid
- Ai Ml Engineer Intern
- Quantum Machine Learning Engineer
- Ai Ml Analyst
- Privacy Preserving Machine Learning
What cities in Ohio are hiring for Adversarial Machine Learning jobs? Cities in Ohio with the most Adversarial Machine Learning job openings:
Deloitte rating
8.1
Based on 86 frontline employees who took The Breakroom Quiz
60th of 138 rated financial services
Job description
Job Summary:
Deloitte is a leading consulting firm focused on transforming the nature of work through innovative solutions. They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and collaborating with various stakeholders.
Responsibilities:
• Lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption.
• Design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring.
• Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
• Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases.
• Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
• Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
• Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
• Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
• Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
• Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
• Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
• Be responsible for the successful execution of AI-powered applications using agile methodology.
• Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
• Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
• 6+ years of consulting experience leading delivery teams, including onshore and offshore team members
• 6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
• 5+ years of experience working in an AI environment
• 5+ years of experience translating requirements into client ready design documents
• 5+ years of experience in software application architecture analysis, design, and delivery
• 5+ years of experience executing full system development life cycle implementations
• Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.
Preferred:
• Advanced degrees such as Masters or PhD are preferred
• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• 5 + years of experience in Data Science, Statistics, and Machine Learning
• 5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
• 5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
• 5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
Company:
Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in London, GBR, with a team of 10001+ employees. The company is currently Late Stage.
Deloitte is a leading consulting firm focused on transforming the nature of work through innovative solutions. They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and collaborating with various stakeholders.
Responsibilities:
• Lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption.
• Design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring.
• Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
• Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases.
• Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
• Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
• Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
• Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
• Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
• Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
• Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
• Be responsible for the successful execution of AI-powered applications using agile methodology.
• Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
• Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
• 6+ years of consulting experience leading delivery teams, including onshore and offshore team members
• 6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
• 5+ years of experience working in an AI environment
• 5+ years of experience translating requirements into client ready design documents
• 5+ years of experience in software application architecture analysis, design, and delivery
• 5+ years of experience executing full system development life cycle implementations
• Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.
Preferred:
• Advanced degrees such as Masters or PhD are preferred
• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• 5 + years of experience in Data Science, Statistics, and Machine Learning
• 5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
• 5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
• 5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
Company:
Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in London, GBR, with a team of 10001+ employees. The company is currently Late Stage.