What are the responsibilities and job description for the AI Platform Architect position at Planet Pharma?
Target PR Range: 100-125/hr DOE
Role Overview
Seeking a forward-thinking AI Platform Architect to design, build, and operationalize enterprise AI platforms that accelerate innovation across research, clinical development, commercial and corporate functions.
This role will lead the architecture and governance of a multi-platform AI ecosystem, spanning AWS (Bedrock, SageMaker), Databricks (Mosaic AI), Claude for Enterprise, and emerging AI-native / agentic engineering tools and platforms. The architect will ensure scalable, secure, and compliant deployment of AI capabilities while enabling rapid experimentation and adoption.
A critical success factor is the ability to stay ahead of industry trends, quickly validate new technologies, and operationalize high-value capabilities in a regulated life sciences environment.
Key Responsibilities
Role Overview
Seeking a forward-thinking AI Platform Architect to design, build, and operationalize enterprise AI platforms that accelerate innovation across research, clinical development, commercial and corporate functions.
This role will lead the architecture and governance of a multi-platform AI ecosystem, spanning AWS (Bedrock, SageMaker), Databricks (Mosaic AI), Claude for Enterprise, and emerging AI-native / agentic engineering tools and platforms. The architect will ensure scalable, secure, and compliant deployment of AI capabilities while enabling rapid experimentation and adoption.
A critical success factor is the ability to stay ahead of industry trends, quickly validate new technologies, and operationalize high-value capabilities in a regulated life sciences environment.
Key Responsibilities
- AI Platform Strategy & Architecture
- 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
- AI Capability Engineering & Operations
- Operationalize reusable AI capabilities:
- Model access layers (LLMs, fine-tuned models)
- Prompt, tool, and agent orchestration frameworks
- Evaluation, monitoring, and observability pipelines
- Enable secure, compliant AI usage (GxP, HIPAA where applicable)
- 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)
- Innovation & Technology Scouting
- Continuously scan AI/ML ecosystem trends (foundation models, agent frameworks, enterprise AI tooling)
- Rapidly prototype and validate emerging capabilities (e.g., new Bedrock models, Mosaic AI features, agent frameworks)
- Create POCs ? pilots ? enterprise rollout pathways
- 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
- 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)
- 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
- Strategic hands-on balance (thinks like an architect, executes like an engineer)
- Ability to translate emerging AI trends into enterprise value
- Strong systems thinking across platforms, data, and workflows
- Excellent stakeholder communication—able to influence senior leadership and engineering teams alike