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Explainable Ai Jobs (NOW HIRING)

Noblesoft Technologies

Gen AI Lead

Dallas, TX · On-site

$138K - $170K/yr

Gen AI & Agents - Prompt Engineering, RAG, Vector DB, Agentic Frameworks, MCP, Large Language Models (LLMs),LangChain, LangGraph, Explainable AI, Conversational AI, Chat bots and Tuning, LLM ...

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 ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Strong experience with optimization techniques (combinatorial, gradient-based, Bayesian), NLP, and Explainable AI * Proficiency in Python, Scala, Java, and/or R * Strong SQL, Hive, SparkSQL skills ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Strong experience with optimization techniques (combinatorial, gradient-based, Bayesian), NLP, and Explainable AI * Proficiency in Python, Scala, Java, and/or R * Strong SQL, Hive, SparkSQL skills ...

Seekr

AI Engineer/Scientist, Staff

Austin, TX · On-site

Our platform helps organizations build, govern, and deploy secure, explainable AI rooted in their own data across cloud, on-premises, edge, and air-gapped environments. We care deeply about ...

Seekr

AI Engineer/Scientist - Staff

Austin, TX · On-site

Our platform helps organizations build, govern, and deploy secure, explainable AI rooted in their own data across cloud, on-premises, edge, and air-gapped environments. We care deeply about ...

Alinia AI

Applied AI Research Intern

Experience with explainable AI (XAI). Why Join Alinia? * Cutting-edge tech: Work on one of the most important challenges in AI--alignment, safety, and trust * Flexible work: Hybrid or remote work ...

Transunion

AI Research & Innovation Lead

Chicago, IL

Drive innovation in areas such as: neuro-symbolic AI, reasoning systems, knowledge-enhanced machine learning, agentic AI, causal AI, explainable AI, human-AI collaboration. * Anticipate AI technology ...

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Explainable Ai information

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$71.5K

$112K

$156.5K

How much do explainable ai jobs pay per year?

As of Sep 2, 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 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.

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 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $112,009 per year, or $53.9 per hour.

Gen AI Lead

Noblesoft Technologies

Dallas, TX • On-site

$138K - $170K/yr

Contractor

Re-posted 2 days ago

Job description

Job Title: Gen AI Lead
Mandatory Skills: Gen AI/Agentic AI /ML
 
JD: 
 
JOB DESCRIPTION:
Keywords: AI/ML Development, Generative AI, LLMs, Python, Web Frameworks, MLOps, Data Engineering
 
Role Overview:
Sr AI/ML Lead with around 15+ years of hands-on experience in developing and implementing Machine Learning and Generative AI solutions. The role involves designing, developing, and deploying end-to-end AI/ML applications using Python, popular ML frameworks, and modern web technologies.
 
 
TECHNICAL SKILLS: Must Have Skills
  • Machine learning development lifecycle - (Data preparation, Data visualization, Statistical Analysis, feature engineering, Predictive modeling, Model deployment, Model monitoring), CI/CD, MLOps, Generative
  • AI, Causal Inference, Time series analysis, Forecasting, Anomaly detection, Hypothesis testing, A/B testing, Git Actions, Tableau, Power BI, ThoughtSpot, Web Scraping
  • Data & Engineering – SQL, MySQL, Postgres, Spark, S3, Trino, Data Factory, ETL, Data pipelines, Databricks and distributed computing.
  • Programming Languages: SQL, Pyspark, Scala, R, Python, SAS
  • Gen AI & Agents – Prompt Engineering, RAG, Vector DB, Agentic Frameworks, MCP, Large Language Models (LLMs),LangChain, LangGraph, Explainable AI, Conversational AI, Chat bots and Tuning, LLM Evaluations and Cost monitoring, HuggingFace
  • Tools/Framework: Git, TensorFlow, PyTorch, PySpark, AWS, MLflow, Docker, Kubernetes, Databricks, SparkSQL, OpenCV, Azure, YOLO, Scikit-Learn, FastAPI, Flask, Django, Keras, Pandas, NumPy, Polars, SciPy, Matplotlib, Seaborn,Plotly, Streamlit
  • Cloud & MLOps: AWS Sagemaker, Azure ML, or GCP AI Platform; Git, Docker, CI/CD.
 
 
 
Role Activities:
  • Design, develop, and deploy AI/ML and Generative AI models for enterprise and telecom use cases.
  • Build and optimize data pipelines for training, validation, and inference processes.
  • Develop web-based AI applications using frameworks like Flask, FastAPI, or Django.
  • Implement LLM-based solutions such as chatbots, summarization, and RAG-based systems.
  • Collaborate with data scientists, solution architects, and business teams to understand functional requirements and translate them into technical implementations.
  • Participate in proof-of-concept (PoC) development for AI/ML and automation use cases.
  • Conduct model evaluation, fine-tuning, and performance optimization.
  • Work with APIs, data sources, and cloud-based ML services (AWS, Azure, GCP).
  • Follow best practices in MLOps, model versioning, and CI/CD integration.
  • Prepare technical documentation, training materials, and demo presentations.

Domain Skills Requirements:
  • At least 10+ years of experience in AI/ML development and Python-based solutions for Telco/Retail Domains
  • Desired Domain Experienced
    • Telecom BSS & OSS domain and understanding of fixed, mobile, IoT & convergence domains and related markets 
    • Business Systems (BSS)- Understanding of E2E BSS Solutions across Sales, Marketing, Finance, Product Management, Care areas for CSPs. 
    • Knowledge on data integration for telecom industry B/OSS COTS  & Data Models ( Amdocs, NetCracker, CSG etc.)
 
Preferred Qualifications:
  • Certification in AI/ML, Deep Learning, or Generative AI is a plus.