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Explainable Ai Jobs in Georgia (NOW HIRING)

Roberthalf

AI & Workforce Signals Analyst

Atlanta, GA · On-site

Develop explainable AI outputs that help leaders understand not just what is happening in the workforce, but why. * Build agentic AI workflows that automate and augmentpeopleanalytics processes (e.g ...

Protiviti

AI & Workforce Signals Analyst

Atlanta, GA · On-site

Develop explainable AI outputs that help leaders understand not just what is happening in the workforce, but why. * Build agentic AI workflows that automate and augmentpeopleanalytics processes (e.g ...

Protiviti

AI & Workforce Signals Analyst

Atlanta, GA · On-site

Develop explainable AI outputs that help leaders understand not just what is happening in the workforce, but why. * Build agentic AI workflows that automate and augment people analytics processes (e ...

Protiviti

AI & Workforce Signals Analyst

Atlanta, GA · On-site

Develop explainable AI outputs that help leaders understand not just what is happening in the workforce, but why. * Build agentic AI workflows that automate and augment people analytics processes (e ...

Ethos Risk Services

Senior Product Manager

Atlanta, GA · On-site +1

$121K - $160K/yr

Support AI Governance & Compliance - Help maintain explainable AI practices, documentation, and regulatory compliance within a highly regulated insurance environment. * Represent the Product ...

Ethos Risk Services

Senior Product Manager

Atlanta, GA · On-site

$150K - $200K/yr

Support AI Governance & Compliance - Help maintain explainable AI practices, documentation, and regulatory compliance within a highly regulated insurance environment. * Represent the Product ...

Intuit

Staff AI Scientist

Atlanta, GA · On-site

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Staff AI Scientist

Atlanta, GA · On-site

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Staff AI Scientist

Atlanta, GA

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Senior Staff AI Scientist

Atlanta, GA · On-site

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Senior Staff AI Scientist

Atlanta, GA · On-site

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Senior Staff AI Scientist

Atlanta, GA

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

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Explainable Ai information

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.

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 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 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 popular job titles related to Explainable Ai jobs in Georgia?

For Explainable Ai jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Explainable Ai jobs in Georgia look for?

The top searched job categories for Explainable Ai jobs in Georgia are:

What cities in Georgia are hiring for Explainable Ai jobs?

Cities in Georgia with the most Explainable Ai job openings:

Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Georgia as of August 2026, with employment types broken down into 71% Full Time, 19% Part Time, 7% Contract, and 3% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Senior Director, Software Engineering - AI, Ontology

Jobtailor

Atlanta, GA • On-site

$150 - $230/hr

Other

Posted 4 days ago

Job description

  • Define and own end-to-end software architecture for AI/ML-enabled platforms, emphasizing ontologies, semantic models, and knowledge representation
  • Lead scalable, production-grade AI/ML systems, including data pipelines, model lifecycle management, and inference services
  • Drive development and adoption of enterprise ontologies and domain models for interoperability, reasoning, explainability, and data reuse
  • Ensure architectural alignment across cloud, edge, and on-premises deployments
  • Establish engineering best practices for model governance, explainable AI, data quality, security, and compliance
  • Partner with product, data science, and business leaders to identify high-value AI/ML use cases and create engineering roadmaps
  • Guide teams on model selection, training strategies, feature engineering, and MLOps
  • Evaluate and integrate emerging AI technologies, frameworks, and tools
  • Advise senior leaders on complex architectural and AI concepts
  • Influence enterprise standards and long-term AI, data, and software engineering strategy
  • Collaborate with global engineering, security, legal, and compliance teams on responsible AI deployment
  • Report directly to the VP Chief Technology Officer – Forge & AI
  • Mentor senior architects and influence executive stakeholders
Requirements
  • 10+ years of progressive software engineering experience
  • Experience with cloud-native architectures, distributed systems, and modern software engineering practices
  • Hands-on experience with ontology design, semantic modeling, knowledge graphs, or domain-driven data models
  • Demonstrated success delivering AI/ML-powered production systems at scale
  • Combination of hands-on technical depth and strategic leadership experience
  • Must be a U.S. Person: U.S. citizen, U.S. permanent resident, protected status under asylum or refugee status, or able to obtain export authorization
  • Bachelor’s and advanced degrees in Computer Science, Engineering, AI, Data Science, or a related field are valued
  • Experience with MLOps platforms, model governance, and AI lifecycle management
  • Familiarity with explainable AI, ethical AI, and regulatory considerations in enterprise environments
  • Prior experience in industrial, enterprise, or highly regulated domains
  • Ability to lead senior technical talent and influence across organizational boundaries
Core Competencies

Demonstrates expertise in defining and owning software architecture for AI/ML platforms, with a strong focus on ontologies, semantic models, and model governance. Proven ability to lead scalable AI/ML systems and collaborate with cross-functional teams to drive high-value use cases and engineering roadmaps.

Highest-signal resume keywords
  • AI/ML System Development
  • Ontology Design
  • MLOps Platforms
  • Cloud-Native Architectures
  • Model Governance
ATS Optimization Keywords Hard Skills
  • Software Engineering
  • Semantic Modeling
  • Knowledge Representation
  • Data Pipeline Management
  • Model Lifecycle Management
  • Feature Engineering
  • Explainable AI
  • Data Quality Assurance
  • Security Compliance
  • Distributed Systems
Soft Skills
  • Strategic Leadership
  • Mentoring
  • Collaboration
  • Influencing Stakeholders
  • Cross-Organizational Communication
Industry Keywords
  • Enterprise Ontologies
  • Regulatory Compliance
  • Ethical AI
  • Industrial Domains
  • Data Reuse
Tools & Technologies
  • AI Frameworks
  • MLOps Tools
  • Cloud Platforms
  • Data Science Tools
  • Knowledge Graphs
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