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Explainable Ai Jobs (NOW HIRING)

ClifyX

GenAI Engineer

Wilmington, DE · On-site

... explainable AI (XAI) for transparent decision-making • Optimize cost, latency, and scalability of AI systems • Troubleshoot AI/ML system issues across data and deployment layers • Write ...

Tradeweb Markets

AI/ML Lead

New York, NY · On-site

$112K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Group Details We are seeking an experienced AI/LLM Product Engineer to design and build intelligent ... Build confirmation and validation flows to ensure outputs are accurate, explainable, and auditable

Keysight Technologies, Inc.

R&D Engineer

Calabasas, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Explainable AI & Diagnostic Analytics Goal: Make engineering models transparent, interpretable, and auditable. * Integrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or ...

Keysight Technologies, Inc.

R&D Engineer

Calabasas, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Explainable AI & Diagnostic Analytics Goal: Make engineering models transparent, interpretable, and auditable. * Integrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or ...

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Explainable Ai information

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$71.5K

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How much do explainable ai jobs pay per year?

As of Aug 12, 2026, the average yearly pay for explainable ai in the United States is $112,009.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,500.00 and $127,000.00 per year, depending on experience, location, and employer.

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.
More about Explainable Ai jobs
What cities are hiring for Explainable Ai jobs? Cities with the most Explainable Ai job openings:
What states have the most Explainable Ai jobs? States with the most job openings for Explainable Ai jobs include:
What job categories do people searching Explainable Ai jobs look for? The top searched job categories for Explainable Ai jobs are:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $112,009 per year, or $53.9 per hour.

GenAI Engineer

ClifyX

Wilmington, DE • On-site

Full-time

Re-posted 25 days ago

Job description

Job Summary:
ClifyX is a company focused on innovative technology solutions, and they are seeking a GenAI Engineer to design and develop AI and Machine Learning solutions specifically for banking applications. The role involves building and deploying models, ensuring compliance, and collaborating with various teams to enhance AI capabilities.
Responsibilities:
• Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.
• Build, fine-tune, and deploy ML models and LLMs for credit scoring, AML, and automation
• Implement RAG-based GenAI applications using internal banking data
• Develop scalable data pipelines for training, validation, and real-time inference
• Collaborate with risk, compliance, finance, and business teams for AI solutions
• Ensure regulatory compliance and AI governance standards
• Implement data security, privacy, and access control mechanisms
• Integrate AI models into production using APIs and microservices
• Apply prompt engineering and model optimization techniques
• Monitor model performance, drift detection, and continuous improvement
• Develop explainable AI (XAI) for transparent decision-making
• Optimize cost, latency, and scalability of AI systems
• Troubleshoot AI/ML system issues across data and deployment layers
• Write efficient Python code using AI frameworks
• Follow MLOps best practices (CI/CD, automated deployment)
• Ensure responsible AI practices (bias, fairness, ethics)
• Mentor teams and contribute to enterprise AI platforms.
Qualifications:
Required:
• Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.
• Build, fine-tune, and deploy ML models and LLMs for credit scoring, AML, and automation
• Implement RAG-based GenAI applications using internal banking data
• Develop scalable data pipelines for training, validation, and real-time inference
• Collaborate with risk, compliance, finance, and business teams for AI solutions
• Ensure regulatory compliance and AI governance standards
• Implement data security, privacy, and access control mechanisms
• Integrate AI models into production using APIs and microservices
• Apply prompt engineering and model optimization techniques
• Monitor model performance, drift detection, and continuous improvement
• Develop explainable AI (XAI) for transparent decision-making
• Optimize cost, latency, and scalability of AI systems
• Troubleshoot AI/ML system issues across data and deployment layers
• Write efficient Python code using AI frameworks
• Follow MLOps best practices (CI/CD, automated deployment)
• Ensure responsible AI practices (bias, fairness, ethics)
• Mentor teams and contribute to enterprise AI platforms.
• Languages: Python
• AI/ML & GenAI: Machine Learning, Deep Learning, LLMs, Prompt Engineering, Fine-tuning
• Frameworks: TensorFlow, PyTorch
• GenAI Tools: LangChain, LlamaIndex
• Vector DB: Pinecone, FAISS
• Cloud Technologies: AWS / Azure / GCP
• Data Pipelines: ETL/ELT, Real-time & Batch Processing
• Integration: APIs, Microservices
• Concepts: RAG Architecture, XAI, Model Optimization
• Methodologies: Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment)
• Compliance: Banking regulations (SR 11-7, GDPR), Model Risk Management
• Soft Skills: Strong communication, stakeholder management, and analytical thinking
Company:
ClifyX provides innovative business solutions which satisfy requirements for mission-critical reliability, scalability, interoperations. Founded in 1998, the company is headquartered in South Plainfield, USA, with a team of 501-1000 employees. The company is currently Late Stage.
ClifyX logo

About ClifyX

Sourced by ZipRecruiter

ClifyX is a well-established player in the IT Services sector that specializes in providing result-oriented technological solutions to a wide range of industrial verticals. Based in South Plainfield, New Jersey, ClifyX offers a comprehensive selection of IT services that include project staffing, application development, professional consulting, and other IT-based solutions. While the company's website, clifyx.com, does not divulge the exact founding date, it is clear that ClifyX has grown into a renowned name within their domain, thanks to their unwavering commitment to innovative practices. The company's mission statement revolves around harnessing the power of technology to assist their clientele in steering their respective businesses towards success.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

South Plainfield, NJ, US

Year founded

1998

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