AI Platform Architect
Planet Pharma · Alameda, CA · Yesterday
EngineeringContract
Key Responsibilities
- Help define and mature enterprise AI platform architecture across cloud and data ecosystems
- Design interoperable solutions spanning: AWS AI stack (Bedrock, SageMaker, model hosting, orchestration)
- Databricks / Mosaic AI (ML lifecycle, feature engineering, LLM ops)
- Claude for Enterprise (secure conversational AI and enterprise workflows)
- Establish patterns for multi-model orchestration, RAG architectures, and agent frameworks
- Operationalize reusable AI capabilities: Model access layers (LLMs, fine-tuned models)
- Prompt, tool, and agent orchestration frameworks
- Evaluation, monitoring, and observability pipelines
- Implement AI platform guardrails: Data access controls
- Responsible AI policies
- Auditability and traceability
- Agentic & AI-Native Engineering (Next-Gen SDLC)
- Drive adoption of agentic software development lifecycle (SDLC) practices
- Define frameworks for: Spec-driven agentic development (Claude Code, Github Copilot, code agents)
- Autonomous/semiautonomous agents across workflows
- Integrate AI-native platforms into enterprise engineering workflows (CI/CD, DevSecOps)
- Enable cross-functional enablement: Partner with: Cloud Engineering, Data engineering, and platform teams for integration patterns
- Security, compliance, and governance stakeholders
- Develop architectural guidance and design standards to engineering teams
- Enable internal adoption through reference architectures, playbooks, and reusable assets
Required Qualifications
- 7+ years in Enterprise architecture, data platforms, or AI/ML engineering
- 5+ years hands-on experience with cloud AI platforms (AWS preferred)
- Proven experience with: AWS Bedrock and/or SageMaker
- Databricks (including Mosaic AI or MLflow ecosystem)
- Enterprise LLM platforms (e.g., Claude, OpenAI, or similar)
- Strong understanding of LLM architectures (RAG, fine-tuning, embeddings, Vector DBs, Graph DBs, Multi agent orchestration)
Preferred Qualifications
- Experience in life sciences, pharma, or clinical trial ecosystems
- Familiarity with: GxP validation processes for AI/ML systems
- Clinical/regulatory data workflows
- Experience designing secure, compliant systems in regulated environments (life sciences strongly preferred)
- Exposure to: Agent frameworks (LangChain, Semantic Kernel, etc.)
- AI observability and evaluation tooling
- Multi-cloud / hybrid architectures