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Inspira Financial · 5 hours ago

AI Solution Architect (Remote)

United States

Full-time

Remote

Senior Level, Lead/Staff

$106K/yr - $134K/yr

8+ years exp

Inspira Financial is a company focused on helping businesses and individuals thrive in their health and wealth journeys. The AI Solution Architect will work closely with product, design, and engineering teams to deliver AI-driven solutions, ensuring security, reliability, and scalability while guiding teams on applying AI tools to solve real business problems.

Financial Services

Responsibilities

Own end‑to‑end solution architectures for AI and AI‑enabled products (discovery → design → deployment), ensuring security, reliability, cost efficiency, and maintainability across cloud and on‑prem boundaries

Establish reference architectures and reusable patterns for GenAI and applied AI (RAG, agents/orchestration, vector search, prompt & tool design, event‑driven microservices, API gateways)

Select fit‑for‑purpose models and services (e.g., Azure OpenAI/Bedrock/Vertex, OSS LLMs, embedding models) with clear tradeoffs on performance, latency, privacy, and cost

Partner with product and platform teams to ship solutions: define requirements, review designs and PRs, and drive prototype → pilot → production with CI/CD, IaC, and MLOps/LLMOps (model versioning, prompt/config management, evals, drift & safety monitoring)

Coach teams to use copilots (e.g., GitHub/Claude), agent frameworks (e.g., LangGraph/Semantic Kernel), and integration SDKs responsibly to improve velocity without compromising quality or security

Ensure observability (tracing, guardrails, red‑teaming, cost dashboards), SLOs, and runbooks are in place before cutover

Run architecture discovery with business stakeholders; frame problems, quantify constraints, and map KPIs (time‑to‑first‑value, cost‑to‑serve, task success, CSAT/NPS, deflection rate, accuracy)

Communicate tradeoffs and roadmaps in clear language to executives and non‑technical partners; publish decision records and architecture docs

Raise the bar on engineering excellence—coding standards, design reviews, threat modeling, and documentation

Active coach and mentor to engineers, evangelizing a culture of pragmatic, responsible innovation; contribute to communities of practice

Qualification

AI Solution ArchitectureGoogle Cloud PlatformMLOps/LLMOpsGenAI PatternsSoftware ArchitectureCoachingProblem FramingCommunication SkillsMentoringCollaboration Skills

Required

Establish reference architectures and reusable patterns for GenAI and applied AI (RAG, agents/orchestration, vector search, prompt & tool design, event‑driven microservices, API gateways)

Select fit‑for‑purpose models and services (e.g., Azure OpenAI/Bedrock/Vertex, OSS LLMs, embedding models) with clear tradeoffs on performance, latency, privacy, and cost

Coach teams to use copilots (e.g., GitHub/Claude), agent frameworks (e.g., LangGraph/Semantic Kernel), and integration SDKs responsibly to improve velocity without compromising quality or security

Ensure observability (tracing, guardrails, red‑teaming, cost dashboards), SLOs, and runbooks are in place before cutover

Communicate tradeoffs and roadmaps in clear language to executives and non‑technical partners; publish decision records and architecture docs

Raise the bar on engineering excellence—coding standards, design reviews, threat modeling, and documentation

Active coach and mentor to engineers, evangelizing a culture of pragmatic, responsible innovation; contribute to communities of practice

Preferred

Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or equivalent experience

8+ years in software/solution architecture or platform engineering, including 3+ years delivering applied ML/GenAI solutions in production environments

Extensive hands-on experience with Google Cloud Platform (GCP), including Vertex AI, BigQuery, Dataflow, Pub/Sub, and cloud-native microservices, APIs, event streaming, containers/orchestration (Kubernetes/GKE), and Infrastructure as Code (Terraform/Deployment Manager)

Practical expertise with GenAI patterns: Retrieval-Augmented Generation (RAG), vector databases (e.g., BigQuery Vector Search), prompt engineering/evaluation, agent design, function/tool calling, and orchestration using Google AI tools

Strong grasp of MLOps/LLMOps: CI/CD for models and prompts, offline/online evaluation, telemetry, drift and safety monitoring, with a focus on Google's Vertex AI Pipelines, Model Registry, and continuous deployment

Experience in regulated industries (financial services, healthcare), contact center/knowledge management, or workflow automation

Certifications (nice‑to‑have): GCP Architect, Security (e.g., CCSK) or equivalent

Experience designing for security, privacy, and compliance within GCP; proficiency with OAuth/OIDC, secrets management (Secret Manager), data protection, and model/content safety controls aligned with Google's best practices

Exceptional written and verbal communication skills; proven ability to influence and collaborate across product, engineering, security, and business teams