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Explainable Ai Jobs in Massachusetts (NOW HIRING)

Munich Re

Sr. Solution Architect

Lynn, MA

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

Munich Re

Sr. Solution Architect

Lowell, MA

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

Munich Re

Sr. Solution Architect

Cambridge, MA

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

Munich Re

Sr. Solution Architect

Brockton, MA

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

Munich Re

Sr. Solution Architect

Boston, MA

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

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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 Massachusetts?

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

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

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

What cities in Massachusetts are hiring for Explainable Ai jobs?

Cities in Massachusetts with the most Explainable Ai job openings:

Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Massachusetts as of August 2026, with employment types broken down into 73% Full Time, 22% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Senior Data Scientist / ML Engineer (Gen AI)

Boston, MA • On-site

Photon
IT Services • 1 - 10 employees
Learn more about Photonexternal

Other

Posted 16 days ago

Job description

Senior Data Scientist / ML Engineer (Generative AI)

Primary Objective

We are seeking a Senior Data Scientist / ML Engineer specializing in Generative AI to design, evaluate, optimize, and productionize AI/ML solutions for enterprise applications including RAG systems, AI agents, intelligent automation, and model evaluation platforms.

The role focuses on improving AI accuracy and retrieval quality, reducing hallucinations, benchmarking LLMs, and building reliable solutions for enterprise-scale deployments.

Success looks like:measurable gains in retrieval/answer quality, robust evaluation frameworks in production, and clear collaboration with AI engineering to ship governed, reliable GenAI systems.

Key Responsibilities

Primary

  • Design and develop machine learning and Generative AI solutions.
  • Build and optimize RAG pipelines, retrieval strategies, embeddings, and semantic search.
  • Evaluate and benchmark LLMs for accuracy, performance, and reliability.
  • Develop AI evaluation frameworks for hallucination detection, accuracy measurement, bias/toxicity detection, and ground-truth validation.
  • Optimize prompts, models, and retrieval workflows.
  • Collaborate with AI engineering teams to deploy models into production.

Also expected

  • Create training, validation, and testing datasets.
  • Perform model benchmarking, A/B testing, and performance analysis.
  • Fine-tune foundation models when required.
  • Implement model monitoring, observability, and ongoing evaluation processes.

Must-Have Experience & Skills

  • 5 10 years of experience in data science, machine learning, or related applied ML roles.
  • Strong Python programming skills, with Pandas, NumPy, and Scikit-learn.
  • Strong foundation in supervised/unsupervised learning, statistical modeling, feature engineering, and model evaluation techniques.
  • Hands-on Generative AI experience with LLM evaluation, prompt engineering, RAG architectures, embedding models, fine-tuning approaches, and agent evaluation frameworks.
  • Experience with PyTorch and/or TensorFlow.
  • Exposure to OpenAI models, Claude, Gemini, and/or open-source LLMs.
  • Experience with vector databases, semantic search, and retrieval optimization.
  • Experience delivering or supporting production AI/ML solutions in enterprise environments.
  • Experience working with distributed onshore/offshore teams.

Preferred Skills

  • Databricks, MLflow, and Spark.
  • GraphRAG and Knowledge Graphs; exposure to Neo4j.
  • Responsible AI, Explainable AI, and AI governance.
  • Banking or Financial Services domain experience.
  • Familiarity with Azure AI Foundry, AWS Bedrock, Kubernetes, and AI observability platforms.

Soft Skills

  • Clear communication with engineering and business stakeholders.
  • Ability to translate evaluation results into actionable model/product decisions.
  • Comfortable owning quality metrics and trade-offs (accuracy, latency, cost, risk) in a delivery setting.

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