AI Solution Architect
About The Position
The AI Solution Architect will contribute to the evolution of the company-wide AI, Data & Analytics platform and shape the architecture of a next-generation data platform using Snowflake, AWS, GCP, and AI technologies. As part of the Data Analytics & Insights team, the role designs and delivers scalable, high-performance solutions supporting organizational Data and AI initiatives. The position is eligible for the Flex worker persona, with work from the Boston or Hingham office once per month. The role focuses on architecture for specific solutions and projects, making design decisions within the framework established by the Senior AI Architect. It collaborates with governance, risk, security, privacy, regulatory, and Enterprise Architecture partners, and provides technical guidance and mentorship to Generative AI Engineers and Data Engineers.
Requirements
- 10+ years of overall IT experience.
- 5+ years of solution architecture experience.
- 3+ years of solution implementation expertise with REST/SOAP APIs.
- 5+ years of hands-on experience with a programming language such as C#, Java, SQL, or Python.
- Extensive experience in data architecture, data engineering, or enterprise architecture, with leadership responsibility in large-scale, complex environments.
- Strong background in enterprise data platforms, including cloud-based data lakes, warehouses, and integration architectures.
- Experience applying data structures, algorithms, and software engineering principles to project requirements; using Snowflake to process and analyze large datasets; and using cloud-native tools such as AWS to optimize performance and scale resources.
- Experience building a semantic store so AI agents and business users share a single source of truth for metrics and definitions.
- Experience with Snowflake AI features, including Cortex, Snowpark, and native AI/ML capabilities.
- Hands-on experience with agentic design patterns such as RAG, orchestrator–worker, planner–executor, and collaborative multi-agent architectures.
- Experience architecting solutions to ingest, store, and index unstructured data such as PDFs, images, and audio using vector databases for RAG.
- Proficiency in MCP (Model Context Protocol) and A2A (agent-to-agent) standards for interoperable, distributed agent systems.
- Ability to partner with Product Management, Engineering, and Business leaders to translate strategic objectives into architectural strategies.
- Knowledge of cloud security best practices, data privacy regulations, and governance frameworks.
- Bachelor’s degree or higher in Computer Science or a related field.
Nice To Haves
- Knowledge of the healthcare payer domain.
- Solution Architect Associate certification, preferably in GCP or AWS.
Responsibilities
- Design technical solutions and blueprints for AI agents or modules, including data flows, API integrations, and component interactions within Google Agent Space, aligned with the Senior AI Architect’s vision.
- Ensure agent development follows architectural patterns, coding standards, security protocols, and best practices defined by the Senior AI Architect and the CoE.
- Optimize Google Agent Space features and Google Cloud Platform services for performance, cost-efficiency, and scalability.
- Design and contribute to reusable agentic components, libraries, and common services for use across the Bluefield framework.
- Troubleshoot complex architectural and integration challenges encountered during agent development and deployment.
- Guide technical integration of agents with enterprise systems, databases, and external APIs, ensuring robust and secure connections.
- Collaborate with enterprise data governance, AI governance, and risk partners to align architecture with governance, security, privacy, regulatory standards, and model risk management.
- Build relationships with Enterprise Architecture teams to ensure solutions meet technical constraints.
- Lead or significantly contribute to proofs of concept and prototypes for agentic capabilities or technologies, validating feasibility and informing design decisions.
- Create and maintain technical documentation for agent architectures, design decisions, integration points, and deployment configurations.
- Participate in code reviews, design reviews, and architectural assessments to ensure quality, maintainability, and scalability.
- Provide technical guidance and mentorship to junior Generative AI Engineers and Data Engineers.
Benefits
- Paid time off
- Medical insurance
- Dental insurance
- Vision insurance
- 401(k)
- Well-being benefits
- Variable pay
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What This Job Offers
Job Type
Full-time
Career Level
Senior
