Overview
Skills
Job Details
Job Title: Enterprise GenAI Architect
Location: Newark, NJ
Employment Type: Full-Time / Contract
About the Role:
Arohak Inc. is seeking an Enterprise Generative AI Architect to lead the design, integration, and governance of GenAI solutions across global business units. The ideal candidate will architect scalable AI platforms and ensure secure deployment aligned with our client s compliance standards (GxP, HIPAA, GDPR, Responsible AI). You will collaborate with cross-functional stakeholders in R&D, Supply Chain, Commercial, and IT to deliver enterprise-grade AI capabilities.
Key Responsibilities:
AI Architecture & Strategy
- Define and implement GenAI reference architecture, integration patterns, and platform roadmap.
- Evaluate and onboard enterprise AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) into J&J corporate infrastructure.
- Build multi-model orchestration frameworks using LLMs (GPT, Claude, Med-PaLM, etc.).
Platform Engineering & Integration
- Architect secure GenAI infrastructure with integrated MLOps/LangOps, vector databases, RAG pipelines, and data governance.
- Enable APIs, microservices, and knowledge graphs for enterprise content retrieval and GenAI applications.
Governance, Security & Compliance
- Implement Responsible AI, Model Risk Management (MRM), and compliance with HIPAA, GxP, and global privacy regulations.
- Set enterprise guardrails, identity & access frameworks (Azure AD, Okta), and model usage policies.
- Collaborate with Legal, Cybersecurity, Data Privacy teams to ensure ethical AI usage.
Business Transformation & Delivery
- Partner with business leaders to identify GenAI use cases in R&D, Medical Affairs, Supply Chain, Quality, HR, and Commercial.
- Lead Proof of Concepts (PoCs), solution validation, and enterprise rollouts.
- Build reusable enterprise accelerators, prompt engineering libraries, and AI Centers of Excellence (CoE).
Required Skills & Experience
- 10+ years in Enterprise Architecture / AI / Data Engineering.
- 3+ years building or deploying GenAI/LLM platforms at scale.
- Strong experience with cloud (Azure preferred), Kubernetes, Databricks, Vector DBs (Pinecone, Weaviate, Azure Cognitive Search).
- Proficiency in RAG, LangChain/LangGraph, LLM Ops, Multi-Agent Systems.
- Knowledge of healthcare and life sciences regulatory environments.
- Expertise in API integration, data pipelines, microservices, and security frameworks.
Preferred Qualifications
- Experience in pharmaceutical, medical devices, or healthcare environments.
- AI certifications (OpenAI, Azure AI, NVIDIA, Google Cloud, etc.).
- Familiarity with Veeva, GxP validation, FAIR data principles.
- Experience building internal AI marketplaces or enterprise knowledge platforms.