1

Ai Solution Architect Jobs (NOW HIRING)

MarTech AI Solution Architect

Manhattan, NY · On-site

$150K - $175K/yr

About the Role We are seeking a MarTech AI Solution Architect to help design and deliver next-generation AI-enabled marketing and customer engagement solutions for enterprise clients across Financial ...

Are you ready to be a pivotal force in shaping the technological landscape of a major enterprise? We're seeking a visionary architect to drive the next generation of cloud and AI solutions. This is ...

WI · On-site

$134.40 - $176.40/hr

Solution Architect - AI focused (Houston/Dallas) Are you an AI-focused Solution Architect looking for the opportunity to deliver Microsoft AI-centric solutions across Azure AI, Copilot, Modern Work ...

Senior AI Solution Architect

Boston, MA · On-site

$144 - $268/hr

Design solutions that are scalable, secure, cost-optimized, and aligned to enterprise architecture standards from inception * Map AI use cases to measurable business value, ensuring that ...

Showing results 41-60

Ai Solution Architect information

See salary details

$16

$70

$95

How much do ai solution architect jobs pay per hour?

As of Aug 31, 2026, the average hourly pay for ai solution architect in the United States is $70.17, according to ZipRecruiter salary data. Most workers in this role earn between $60.58 and $79.81 per hour, depending on experience, location, and employer.

What does an AI Solution Architect do?

An AI Solution Architect is responsible for designing and overseeing the implementation of artificial intelligence solutions within an organization. They assess business needs, select appropriate AI technologies, and create scalable system architectures that integrate AI models and data pipelines. AI Solution Architects collaborate with data scientists, software engineers, and stakeholders to ensure that AI solutions are efficient, secure, and aligned with business goals. Their role often includes evaluating new technologies, managing project timelines, and ensuring that solutions meet regulatory and ethical standards.

How does an AI Solution Architect typically collaborate with data scientists and engineering teams during a project?

As an AI Solution Architect, you play a central role in bridging the gap between data scientists, engineering teams, and business stakeholders. You work closely with data scientists to understand their modeling requirements and ensure that the AI solutions are scalable and production-ready. On the engineering side, you help design the system architecture, integrate AI models into existing platforms, and address technical constraints. Effective communication and collaboration are key, as you often translate complex technical details into actionable plans for both teams to execute successfully.

What are the key skills and qualifications needed to thrive as an AI Solution Architect, and why are they important?

To thrive as an AI Solution Architect, you need a deep understanding of machine learning, data science, software engineering, and cloud architecture, typically supported by a degree in computer science or related field. Familiarity with tools like TensorFlow, PyTorch, cloud platforms (AWS, Azure, GCP), and relevant certifications such as AWS Certified Machine Learning or Google Professional Machine Learning Engineer is highly valuable. Strong communication, problem-solving, and stakeholder management skills set top performers apart in this role. These skills are crucial for designing effective AI solutions that meet business goals while ensuring technical feasibility and stakeholder alignment.

What is the difference between Ai Solution Architect vs Data Scientist?

AspectAi Solution ArchitectData Scientist
Required CredentialsTypically requires a degree in computer science, AI, or related fields; certifications in AI/ML are commonRequires a degree in statistics, mathematics, or computer science; certifications in data analysis or machine learning are beneficial
Work EnvironmentDesigns AI solutions, collaborates with engineering teams, and oversees implementationAnalyzes data, builds models, and interprets results to inform business decisions
Employer & Industry UsageUsed in tech companies, AI-focused firms, and enterprises implementing AI solutionsCommon in research institutions, tech companies, and industries relying on data analysis

The main difference is that Ai Solution Architects focus on designing and implementing AI systems, while Data Scientists analyze data and develop models. Both roles require strong technical skills and often collaborate, but their core responsibilities differ in scope and focus.

How much does an AI Solution Architect make?

An AI Solution Architect typically earns between $100,000 and $160,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in machine learning and cloud platforms can earn higher salaries, often exceeding $180,000.

What cities are hiring for Ai Solution Architect jobs?

Cities with the most Ai Solution Architect job openings:

What are the most commonly searched types of Ai Solution Architect jobs?

The most popular types of Ai Solution Architect jobs are:

What states have the most Ai Solution Architect jobs?

States with the most job openings for Ai Solution Architect jobs include:

Infographic showing various Ai Solution Architect job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution, with an average salary of $145,963 per year, or $70.2 per hour.

Senior AI Solution Architect

Boston Scientific Gruppe

Marlborough, MA • On-site

$106.80 - $202.90/hr

Other

This job post has expired 4 days ago. Applications are no longer accepted.


Job description

Additional Location(s): US-MA-Marlborough; US-MN-Arden Hills

Diversity - Innovation - Caring - Global Collaboration - Winning Spirit- High Performance

About the role:

Boston Scientific is seeking a Senior AI Solution Architect to join our AI Engineering team and lead the design of next-generation AI solutions across the enterprise. In this role, you will operate at the intersection of business strategy and advanced technology - translating complex business challenges into scalable, secure and compliant generative AI and agentic AI architectures. You will define end‑to‑end technical solution architectures for AI‑powered products, including custom generative AI applications, intelligent agents, virtual assistants and reusable AI services. This role requires deep technical expertise, strong architectural judgment and the ability to influence cross‑functional stakeholders across engineering, data, cybersecurity, legal and business teams.

Work model, sponsorship, relocation:

At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our Minnesota or Massachusetts office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time.

Your responsibilities will include:
  • Lead the end‑to‑end architecture of enterprise AI solutions, including generative AI applications, large language model‑powered workflows, agentic systems and intelligent automation.
  • Design modular and reusable AI components and services leveraged across multiple platforms and business use cases.
  • Define architectural patterns for agent orchestration, tool integration, memory management, retrieval‑augmented generation and human‑in‑the‑loop workflows.
  • Translate business requirements into scalable, production‑ready AI architectures aligned with enterprise standards.
  • Partner with business stakeholders to understand objectives, constraints and value drivers, ensuring measurable business impact.
  • Collaborate with AI engineers, software engineers, data scientists and data engineers to guide implementation and ensure architectural integrity.
  • Partner with enterprise architecture, cybersecurity, legal, privacy, quality and platform engineering teams to ensure solutions meet regulatory, security and quality expectations.
  • Architect secure and scalable data pipelines in partnership with data engineering teams to support AI and generative AI workloads.
  • Evaluate and integrate technologies across Azure, AWS and Snowflake to deliver cloud‑native, resilient and cost‑effective solutions.
  • Guide platform‑level decisions related to model hosting, vector databases, orchestration frameworks, monitoring and MLOps/LLMOps practices.
  • Ensure solutions are designed for performance, reliability, observability and operational excellence.
  • Embed ethical AI, security‑by‑design, privacy‑by‑design and compliance‑by‑design principles into all solution architectures.
  • Support risk assessments, model reviews and required documentation for enterprise and regulated environments.
Required qualifications:
  • Minimum Bachelor's or Master's degree in computer science, engineering, data science or a related technical field.
  • Minimum of 5 years' experience in solution architecture, software architecture or AI/ML engineering, including recent hands‑on work in generative AI.
  • Proven experience designing and deploying large language model‑based solutions, including retrieval‑augmented generation, prompt engineering and model integration.
  • Previous background in healthcare, life sciences or other highly regulated industries.
  • Strong understanding of cloud‑native architectures in Azure and/or AWS and modern data platforms such as Snowflake.
  • Demonstrated experience working in enterprise‑scale, regulated environments with security, compliance and quality requirements.
  • Demonstrated ability to communicate complex technical concepts clearly to technical and nontechnical audiences.
Preferred qualifications:
  • Proven experience with agentic AI frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI or similar technologies.
  • Familiarity with vector databases, embedding strategies and search optimization techniques.
  • Preferred hands‑on experience with MLOps/LLMOps, including model monitoring, evaluation and lifecycle management.
  • Proven experience defining reference architectures, design patterns and reusable AI platforms.

Requisition ID: 631216

Minimum Salary: $106,800

Maximum Salary: $202,900

The anticipated compensation listed above and the value of core and optional employee benefits offered by Boston Scientific (BSC) — see www.bscbenefitsconnect.com — will vary based on actual location of the position and other pertinent factors considered in determining actual compensation for the role. Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs. At BSC, it is not typical for an individual to be hired near the bottom or top of the anticipated salary range listed above.

Compensation for non‑exempt (hourly), non‑sales roles may also include variable compensation from time to time (e.g., overtime and shift differential) and annual bonus target (subject to plan eligibility and other requirements).

Compensation for exempt, non‑sales roles may also include variable compensation, i.e., annual bonus target and long‑term incentives (subject to plan eligibility and other requirements).

Boston Scientific Corporation has been and will continue to be an equal opportunity employer. To ensure full implementation of its equal employment policy, the Company will continue to take steps to assure that recruitment, hiring, assignment, promotion, compensation and all other personnel decisions are made and administered without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, gender expression, veteran status, age, mental or physical disability, genetic information or any other protected class.

#J-18808-Ljbffr