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Ai Automation Testing Jobs in Massachusetts (NOW HIRING)

1 point system

Senior AI/Automation Engineer

Boston, MA · On-site

$113K - $148K/yr

Requirement - Senior AI/Automation Engineer Location- Boston, MA and Minneapolis, MN(4 days a week ... testing, deployment, and auditability. · Drive spec-to-code, code-to-test, and test-to-deployment ...

Wilmerhale

AI Automation Engineer

Boston, MA

Propose solutions (including automation and use of AI); identifying alignment with business ... Responsible for the design, development, testing, and deployment of new applications and ...

WilmerHale

AI Automation Engineer

Boston, MA · On-site

$135K - $168K/yr

Responsible for the design, development, testing, and deployment of new applications and ... AI agents) * Strong technical background with a focus on workflow automation and process ...

WilmerHale

AI Automation Engineer

Boston, MA · On-site

$135K - $168K/yr

Responsible for the design, development, testing, and deployment of new applications and ... AI agents) * Strong technical background with a focus on workflow automation and process ...

Infosys

Test Automation Consultant

Quincy, MA · On-site

$90K - $120K/yr

... to AI/ML concepts in testing, such as: AI-assisted test case generation Intelligent test ... API automation tools -Playwright API testing, REST Assured, Postman. Additional Required ...

Blitzy

Automation Engineer

Cambridge, MA · On-site

The Automation Engineer will own, extend, and scale automated testing frameworks to ensure the performance of AI-generated applications in production environments. Responsibilities : • You expand ...

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Ai Automation Testing information

What are some common challenges faced by AI automation testing professionals when validating machine learning models?

AI Automation Testing professionals often encounter challenges such as ensuring that test cases comprehensively cover the unique behaviors of machine learning models, dealing with non-deterministic outputs, and handling large datasets efficiently. It's also common to face difficulties in setting up reliable test environments that simulate real-world data scenarios. Collaboration with data scientists and developers is crucial to define meaningful metrics and effectively interpret test results, ensuring the AI system meets both functional and ethical standards.

What is AI automation testing?

AI automation testing refers to the use of artificial intelligence technologies to automate the process of testing software applications. This approach enhances traditional automated testing by using machine learning and data analysis to identify test cases, detect defects, and optimize test coverage. AI-driven testing tools can adapt to changes in the application, reduce manual effort, and improve the accuracy and speed of testing processes. As a result, organizations can deliver higher-quality software more efficiently.

What are the key skills and qualifications needed to thrive as an AI automation testing professional?

To thrive as an AI Automation Testing professional, you need a solid understanding of software testing principles, programming languages (such as Python or Java), and AI/ML concepts, often supported by a degree in computer science or a related field. Familiarity with automation tools like Selenium, Appium, and AI-powered testing frameworks, as well as certifications like ISTQB, is highly valued. Strong analytical thinking, attention to detail, and effective communication skills help professionals excel in diagnosing issues and collaborating with development teams. These skills ensure the delivery of robust, efficient, and reliable AI-driven software products in a competitive technology landscape.

What is the difference between Ai Automation Testing vs Software Test Engineer?

AspectAi Automation TestingSoftware Test Engineer
Required CredentialsCertifications in AI, automation tools, programming languagesSoftware testing certifications (ISTQB, CSTE), programming skills
Work EnvironmentFocus on automation frameworks, AI integration, scriptingManual and automated testing, test case design, bug tracking
Employer & Industry UsageTech companies, AI-driven projects, software development firmsSoftware development companies, IT departments, QA teams

Ai Automation Testing and Software Test Engineer roles overlap in testing skills and programming knowledge. However, Ai Automation Testing emphasizes AI integration and automation frameworks, while Software Test Engineers focus more on manual testing, test case creation, and bug identification. Both roles are essential in software quality assurance but serve different aspects of the testing process.

What are popular job titles related to Ai Automation Testing jobs in Massachusetts?

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

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

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

Ai Automation Testing jobs near you
Infographic showing various Ai Automation Testing job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Senior AI/Automation Engineer

1 point system

Boston, MA • On-site

$113K - $148K/yr

Contractor

Re-posted 8 days ago

Job description

Requirement - Senior AI/Automation Engineer

Location- Boston, MA and Minneapolis, MN(4 days a week on-site)

Mode of Interview : 3 rounds, all virtual

Contract W2

We are seeking a Senior AI Automation Engineer to lead the next phase of AI-enabled SDLC maturity across Investment Technology. This role is responsible for defining, building, and scaling AI-driven automation across the software delivery lifecycle—leveraging Anthropic Cloud Code–based capabilities, agentic workflows, and enterprise-grade GenAI platforms.
The ideal candidate brings hands-on experience operationalizing LLMs at scale, deep understanding of modern SDLC practices, and a strong bias toward automation, governance, and measurable business outcomes. This role will directly influence how software is designed, built, tested, and deployed across Ameriprise.
AI-Enabled SDLC Transformation
· Define and execute a GenAI-augmented SDLC strategy, embedding AI across requirements, design, development, testing, deployment, and auditability.
· Drive spec-to-code, code-to-test, and test-to-deployment automation using LLM-powered workflows aligned to enterprise SDLC standards 1.
· Partner with Architecture, DevSecOps, Risk, and Compliance teams to ensure secure, governed, and auditable AI adoption.
Anthropic Cloud Code & Agentic Automation
· Serve as the subject matter expert for Anthropic Cloud Code capabilities, including:
o Prompt engineering standards
o Agent-based orchestration patterns
o Secure model invocation and policy enforcement
· Design and deploy agentic AI workflows to automate:
o Requirements for elaboration and decomposition
o Jira story and acceptance criteria generation
o Unit, integration, and UAT test generation
o SDLC artifact and control evidence creation
Enterprise Platform Enablement
· Integrate AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).
· Establish reusable AI components, frameworks, and guardrails for product and engineering teams.
· Enable adoption through reference architectures, implementation patterns, and developer enablement.
Value Realization & Measurement
· Identify and quantify productivity, quality, and cycle-time improvements driven by AI.
· Define KPIs and success metrics tied to SDLC efficiency, developer experience, and risk reduction.
· Support executive visibility into AI-driven outcomes and maturity progress.
Preferred Qualifications
· Experience integrating GenAI into requirements management, testing automation, and SDLC controls.
· Familiarity with enterprise GenAI governance models, including model risk management and auditability.
· Experience working in financial services or highly regulated industries.
· Thought leadership in AI platforms, automation, or engineering productivity initiatives.
What Success Looks Like
· AI-driven automation measurably reduces SDLC cycle time and manual effort.
· Development teams consistently leverage AI-generated requirements, code, and tests within governed workflows.
· Anthropic Cloud Code capabilities are adopted as a standard, reusable enterprise platform capability.
· Ameriprise achieves a demonstrable increase in SDLC maturity, quality, and