AI Solution Architect

Ready to help us transform healthcare? Bring your true colors to blue.

The AI Solution Architect is responsible for designing and implementing specific architectural components and solutions for AI agents within the established Bluefield framework. This role works in close collaboration with Generative AI Engineers to convert architectural guidelines into practical, deployable systems, ensuring high technical quality at the project level. The ideal candidate will also act as a technical evangelist, sharing knowledge and showcasing the team's work through internal and external tech talks.

Responsibilities

Design agentic workflows — Architect multi-agent systems on Vertex AI Agent Builder, defining agent orchestration patterns (sequential, parallel, hierarchical), tool use strategies, function calling, and grounding configurations (Google Search, Vertex AI Search, custom data stores)
Build architecture blueprints — Produce solution design documents, architecture decision records, and integration patterns that connect AI agents with APIs, databases, and enterprise platforms
Select and optimize models — Evaluate foundation models (Gemini family, open models via Model Garden) for cost, latency, accuracy, and context window fit; design prompt strategies including chain-of-thought, few-shot, and system instructions
Architect RAG pipelines — Design retrieval-augmented generation flows using Vertex AI Search, Vector Search, and custom data stores to ground agent responses in enterprise knowledge
Define guardrails and safety — Establish input/output validation, content filtering, hallucination mitigation, and human-in-the-loop checkpoints to ensure responsible, reliable agent behavior
Design for observability — Build tracing, logging, and monitoring into agent architectures to track reasoning chains, tool call performance, latency, cost, and quality metrics
Lead proof-of-concepts — Execute PoCs to validate architectural decisions, de-risk new patterns, and demonstrate feasibility before committing to full builds
Enable the team — Provide architectural guidance, conduct design reviews, create reusable templates and reference architectures, and help developers ramp up on the agent platform
Collaborate across teams — Work with data engineering, platform engineering, Network Security, and business stakeholders to align AI solutions with enterprise standards, security requirements, and business goals
Stay ahead of the curve — Track GCP platform updates, new agent capabilities, and emerging patterns in the rapidly evolving agentic AI landscape, and bring those insights back to the team

Must-Have Qualifications & Skills

Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent practical experience.
8–10+ years in solution architecture, software architecture, or a senior technical role specifically in cloud architecture (GCP, AWS, or Azure).
3+ years of hands-on experience designing and deploying production Generative AI systems, including Retrieval-Augmented Generation (RAG), vector databases, and semantic search.
Proven experience designing multi-agent systems and orchestration workflows (e.g., Agent Development Kit (ADK), LangGraph, CrewAI, AutoGen).
Strong LLM fundamentals — prompt engineering, embeddings, vector search, fine-tuning, and distillation
Deep understanding of machine learning foundations, NLP, model evaluation, fine-tuning, and LLM safety/guardrails.
Hands on experience in designing MLOps / LLMOps pipelines, including continuous integration/deployment (CI/CD), data versioning, model monitoring, and governance in cloud environments.
Expertise in cloud-native solution design ensuring enterprise-grade scalability, performance, data privacy, and security compliance.
Proven track record leading architecture design reviews (ADRs), establishing reference architectures, and defining best practices.
Strong ability to bridge technical and business domains, delivering executive-level technical presentations and articulating business value to senior stakeholders.
Demonstrated experience providing technical mentorship to engineering teams and cross-functional technical leadership.
Certifications:
Professional Cloud Architect or AI/ML Specialty certification (Google Cloud Professional Cloud Architect / Machine Learning Engineer, AWS Certified Solutions Architect – Professional, or equivalent).

Nice-to-Have

Knowledge of the Healthcare payer domain.
Experience with multi-modal agents (vision, audio, document processing)
Experience building evaluation frameworks for agent quality (benchmarks, LLM-as-judge patterns)

What You Bring

A passion for pushing the boundaries of what AI agents can do in real-world enterprise settings
The ability to think end-to-end — from a business problem through to a production-grade architecture with monitoring, security, and cost controls baked in
Comfort operating in a fast-moving space where the tools and best practices are evolving monthly
A builder's mindset — you don't just draw diagrams, you validate your designs through PoCs and hands-on prototyping
A collaborative spirit — you elevate the team around you through mentorship, reusable patterns, and clear documentation

What You Will Gain

High visibility across the organization — present to leadership, influence technology direction, and drive build-vs-buy decisions
Opportunity to build and mentor a growing AI engineering team
Autonomy to explore, prototype, and champion new architectural approaches

Minimum Education Requirements

High school degree or equivalent required unless otherwise noted above

Location

Boston

Time Type

Full time

Salary Range: $175,860.00 - $214,940.00

The job posting range is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee’s pay position within the salary range will be based on several