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Explainable Ai Jobs in Washington (NOW HIRING)

AI/ML Subject Matter Expert

Vienna, VA · Remote

$195K - $210K/yr

... and Explainable AI practicesRegularly recommend and report on industry trends and solutions to meet evolving client needs regarding techniques, tools and technologies for AI/ML integration.

Staff Solutions Architect (U.S. Government)

Reston, VA · On-site

$65.50 - $86.25/hr

Seekr is building trusted, explainable AI solutions for mission-critical environments. We are seeking a Solutions Architect to help U.S. Government customers understand and adopt our AI platform and ...

Senior Data Scientist

Rockville, MD · On-site

$131.30 - $237.35/hr

Apply cutting‑edge techniques in statistical analysis, predictive analytics, entity resolution, graph analytics, explainable AI, and operational analytics. * Work with diverse data types ...

Showing results 41-60

Explainable Ai information

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.
What are popular job titles related to Explainable Ai jobs in Washington? For Explainable Ai jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Washington look for? The top searched job categories for Explainable Ai jobs in Washington are:
What cities in Washington are hiring for Explainable Ai jobs? Cities in Washington with the most Explainable Ai job openings:
Infographic showing various Explainable Ai job openings in Washington as of August 2026, with employment types broken down into 48% Full Time, and 52% Contract. Highlights an 82% In-person, and 18% Remote job distribution.

AI/ML Subject Matter Expert

Halvik

Vienna, VA • Remote

$195K - $210K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 8 days ago


Job description

Halvik Corp delivers a wide range of services to 13 executive agencies and 15 independent agencies. Halvik is a highly successful WOB business with more than 50 prime contracts and 500+ professionals delivering Digital Services, Advanced Analytics, Artificial Intelligence/Machine Learning, Cyber Security and Cutting-Edge Technology across the US Government. Be a part of something special!Responsibilities:Responsible for developing and implementing AI/ML solutions to solve complex problems and enhance business operations.Define direction of mission-critical solutions using best-fit AI/ML algorithms and technologiesWork closely with cross-functional teams to design, develop, and deploy AI models and algorithms that enable data-driven decision making.Conduct statistical analyses using ML techniques for customer-focused solutionsCollaborate with cross-functional teams to deliver world-class solutions for large-scale data processingGuide clients in navigating ML algorithms, tools, and frameworksDesign, develop, and deploy AI models for data-driven decision making and business innovationCreate intelligent systems that automate processes, improve efficiency, and drive business innovation, leveraging their expertise in AL/ML, deep learning, and data analysis.Create, train, and maintain intelligent systems to automate processes and improve efficiencyReport on industry trends and recommend solutions for AI/ML integrationDesign and build ML models solving real-world business problemsMake informed ML infrastructure decisions based on modeling techniques and issuesWrite and test application code, develop ML models, and automate tests and deploymentRetrain, maintain, and monitor production modelsLeverage cloud-based architectures to deliver optimized ML models at scaleConstruct optimized data pipelines for ML modelsImplement CI/CD best practices for ML model and application code deploymentEnsure code security, model governance, and adherence to Responsible and Explainable AI practicesRegularly recommend and report on industry trends and solutions to meet evolving client needs regarding techniques, tools and technologies for AI/ML integration. Requirements:Master's degree or Bachelor's of science with 5+ years of industry experience in Computer Science, Software Engineering, Data Science, Statistics, or related STEM field.5+ years of experience in AI, data science, ML engineering, or related fields4+ years of experience with modern cloud computing technologies (AWS, Databricks, Microsoft Azure or GCP)Experience in fine-tuning LLMs for custom datasets and/or using RAG to augment LLM applications.Knowledge of multiple ways of tackling AI/ML problems and demonstrated ability to make good initial choices to make systems that perform well more quicklyExperience using Generative AI tools to help in software programming and testing (e.g. VS-Code or GitHub with copilot AI)Proficiency in SQL, including advanced query techniquesExperience with version control systems (e.g., Git, Github, Jenkins)6+ years of proficiency in Python and Jupyter notebooks. Experience with other languages such as R, and Scala is a plus but not necessary.Experience developing and assessing AI/ML models and ensemblesSupport the seamless integration of multi-media data processing into platform data pipelinesExperienced in planning, setting-up, running and reporting on AI/ML experiments.Experience building feature repositories for AI-ML model trainingProject work in deep learning, transformers, computer vision, NLP, or chatbot developmentExperience in developing Generative AI applications especially using LLMs in domains such as NLP and image processingKnowledge of modern software design patterns (e.g., microservices, edge computing)Experience working with LLM frameworks and foundation models like LLaMa-3 and experience working with LLM model repositories like HuggingfaceFamiliarity with MLOps practices and tools for model versioning and experiment trackingExperience identifying appropriate AI/ML models for a problem and using appropriate techniques to evaluate performance to choose the best modelExperience with techniques for tuning model parameters to application data and conditions Abilities:Ability to lead an Agile team of data scientists, engineers, architects and testersDemonstrated ability to work independentlyStrong problem-solving skills and ability to think creatively to overcome technical challengesParticipate in the full machine learning lifecycle, from business understanding, data collection and preprocessing to model selection, evaluation, deployment and monitoringAdapt to rapidly changing technologies and methodologies in the AI/ML fieldExcellent communication, analytical, and interpersonal skillsAbility to gather requirements and work effectively as part of an Agile teamCapacity to explain complex technical concepts to non-technical stakeholdersExcellent technical writing skillsCollaborate with business stakeholders to identify opportunities for AI/ML applicationsDevelop and maintain documentation for ML models, including methodology, assumptions, and limitationsMentor junior team members and contribute to the growth of the AI/ML practiceAbility to thrive in a mainly remote work environmentCommitment to continuous learning in the fast-evolving AI/ML landscape and look for ways to apply new techniques when appropriate.Desired Qualifications:Having experience and a good understanding of Ux, Human-AI teaming, human factors in AI and software systemsPublished research papers or patents in the field of AI/MLKnowledge of Agentic AI Systems (e.g. AutoGen, LangGraph, LangChain, CrewAI etc.)Experience with Knowledge Graphs tools and techniquesExperience with federated learning or privacy-preserving machine learning techniquesKnowledge of data visualization techniques and toolsGPU programming experiencePrivacy Policy – Halvik CorpHalvik offers a competitive full benefits package including:Company-supported medical, dental, vision, life, STD, and LTD insuranceBenefits include 11 federal holidays and PTOEligible employees may receive performance-based incentives in recognition of individual and/or team achievements.401(k) with company matchingFlexible Spending Accounts for commuter, medical, and dependent care expensesTuition AssistanceCharitable Contribution matchingHalvik Corp is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.Halvik's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.