Inspira Financial is looking to accelerate the delivery of AI-driven solutions that delight customers and improve business outcomes by guiding engineering teams on the application of AI tools to real business problems.
Requirements
- 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 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.
- 8+ years in software/solution architecture or platform engineering, including 3+ years delivering applied ML/GenAI solutions in production environments.
- GCP Architect, Security (e.g., CCSK) or equivalent certifications (nice-to-have).
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).
Other
- Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or equivalent experience.
- Exceptional written and verbal communication skills; proven ability to influence and collaborate across product, engineering, security, and business teams.
- Ability to work occasional overtime.
- Occasional travel (up to ~15%).
- Occasional after-hours work to support releases or incident response.
