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# Senior AI Solution Architect

- **Company:** Boston, Inc.
- **Location:** Marlborough, MA, United States
- **Experience:** Expert
- **Salary:** $106,800 - $202,900
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Architectural Patterns, Microsoft Azure, Cloud Engineering, Information Engineering, Data Infrastructure, Software Design Patterns, Memory Management, Monitoring of Systems, Industry Standard Architecture, Machine Learning, Language Modeling, Software Architecture, Systems Integration, Retrieval-Augmented Generation, Snowflake, Multi-Agent Systems, Prompt Engineering, Generative AI, Agentic-AI, AI Platforms, Information Technology, Modeling and Simulation, CrewAI, AutoGen, Machine Learning Operations, Semantic Kernel, Data Pipelines, Human in the Loop
- **Published:** November 10, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ai-solution-architect-marlborough-ma--e682e4b2-08d8-4b2c-8da4-c199a34a9633

## About the Role

Artificial Intelligence (AI), Diversity, Team Player, Artificial Intelligence (AI), Architectural Services, Software Agents, Cross-Functional, Enterprise Architecture, Automation, Modeling Languages, * 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.
 * Experience in health care, life sciences or another highly regulated industry.
 * 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., * 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.
 * Hands-on experience with MLOps/LLMOps, including model monitoring, evaluation and lifecycle management.
 * Proven experience defining reference architectures, design patterns and reusable AI platforms.

## Description

* 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 that can be 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, helping ensure measurable business impact.
 * Collaborate with AI engineers, software engineers, data scientists and data engineers to guide implementation and maintain architectural integrity.
 * Partner with enterprise architecture, cybersecurity, legal, privacy, quality and platform engineering teams to help 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 solution architectures.
 * Support risk assessments, model reviews and required documentation for enterprise and regulated environments.