Description

Description:

We are seeking a hands-on Principal / Lead AI Integration Engineer for working in the

Digital Content Supply Chain Lifecycle management technology domain at Genentech as a

member of the Roche Digital Technology team. This individual will design, build and deploy

autonomous AI systems that reason, plan, and execute complex tasks with minimal human

intervention. The goal is to shape and deliver the future of automated, compliant, and

hyper-personalized content creation, review, production and distribution capabilities through the

delivery of Agentic tech stack combined with strong data, asset management, tagging and

meta-data tracking solutions in support of Digital Marketers at Genentech.

Operating at the intersection of our IT and Business teams, you will partner with Data Science

and Machine learning engineers, design, develop and drive the technical execution of our

Generative and Agentic AI roadmap.

Your mission in collaboration with Data Science and AI experts at Genentech, is to transform

traditional enterprise content supply chain workflows into an API-First Headless Agent first

Architecture powered by autonomous AI Agents.

While the ultimate target enterprise deployment stack is primarily AWS-native, we are looking

for the well rounded cloud-native AI engineering minds across AWS, GCP, or Azure who can

design AI Agents that deliver robust functionality individually and work when required in an

orchestrated manner in conjunction with other Agents in order to automate everything from

ingestion and personalized content generation to compliance testing, deployment, message

testing simulation and more. The candidate must have a strong understanding of Semantic,

Knowledge and foundational data layers essential for powering AI solutions.

Strategic Pilot & MVP Focus Areas

As the AI Integration Engineer, you will directly own the technical design, pattern definition, and

delivery of the following high-priority AI initiatives, working closely with Solution and Enterprise

Architects in the space of Digital Content Supply Chain Management:

● AI Chat-Native Workspace: Building an interactive collaboration canvas integrated with

an Insights Engine, Context Ingestion & Content Personalization Layer that powers a

copy creation engine for text based content.

● Next-Gen Creation: Implementing Dynamic Visual Component Pairing & Firefly

Prompting via secure API connections.

● Pilot Core Continuity & Expansion: Drive Claims Optimization and Channel Expansion

via automated cloud workflows.

● Automated Regulatory & Quality Pipelines: Architect the Automated Pre-CMLR

Inspection & Production readiness Pipeline, and Automated validation of Reference &

Citation Blocks.

● Simulation & Optimization: Developing a Sandboxed Digital Twin Outcome Simulator,

Content Effectiveness Scoring, and an Intelligent A/B Testing Workspace.

*Primary Skill Set

Key Responsibilities *

 Enterprise Agentic AI Architecture & Master Orchestration

● System Integration & Orchestration: Design and implement the Master Agentic

Orchestration layer using cloud-native tools (e.g., AWS Step Functions/Bedrock Agents,

GCP Vertex AI, or Azure OpenAI/Semantic Kernel) to interface seamlessly with adjacent

legacy systems.

● End-to-End Content Supply Chain Automation: Map and build multi-agent workflows

that securely source data from Adobe technologies, utilize foundation models to

generate compliant text, extract metadata from digital assets, and push assets into

downstream API-driven consumption layers.

● Guardrails & Compliance Execution: Implement strict operational boundaries using AI

guardrails, content moderation APIs, and serverless computing to guarantee that

AI-generated text and visual components adhere to strict brand, safety, and regulatory

guidelines.

1.⁠ ⁠Testing, Quality Assurance & Message Testing

● Agent Logic Validation: Validate the state management and decision-making logic of

autonomous AI agents using robust ML tracking to ensure automated outputs

consistently meet business rules.

● Simulation & Testing Frameworks: Architect and deploy a Digital Twin Outcome

Simulator for message testing and an Intelligent A/B Testing Workspace leveraging

containerized microservices and clean data rooms to safely model and validate content

efficacy before production.

● Traceability, Auditability & Compliance: Establish full observability for auditability &

Compliance: Setting up end-to-end tracing of agent decisions, logging prompt inputs,

tool calls, and LLM responses to satisfy audit readiness requirements.

1.⁠ ⁠Agile Execution & Data Documentation

● Technical Artifacts: Author and maintain highly technical Epics, user stories,

architecture diagrams, and sequence flows optimized for AI/ML and data developers.

● Insights & Personalization Ingestion: Design data pipelines using streaming data

tools and vector search engines to power the Insights Engine Ingestion & Context

Personalization Layer.

Qualifications & Skills

Experience

● 8+ years of deep technical experience in Cloud Engineering, Data Engineering, or

AI/ML Engineering within enterprise-scale cloud environments (AWS, GCP, or Azure).

● Proven Leadership: Experience acting as a Tech Lead or Principal Engineer, guiding

cross-functional agile teams, and managing high-stakes stakeholder relationships.

● Domain Context: Background in Content Supply Chain, Content Authoring, Modular

Content, and Content Assembly within the Adobe Ecosystem (AEM, DAM, Workfront)

connected to modern cloud stacks is highly preferred.

Technical Skill Set

● Enterprise AI/LLM Orchestration: Advanced experience building autonomous agents

and RAG pipelines using cloud-native AI suites (e.g., Amazon Bedrock, GCP Vertex

AI, or Azure OpenAI Service) and orchestration frameworks (e.g., LangGraph, CrewAI,

AutoGen, or Semantic Kernel).

● Serverless & Microservices: Expert knowledge of designing stateful orchestration and

event-driven architectures using serverless compute (e.g., AWS Lambda/Step

Functions, Google Cloud Functions, or Azure Functions) and secure API patterns

(REST, GraphQL).

● Advanced RAG & Semantic Layers: Capability to design graph-based knowledge

retrieval systems (Knowledge Graphs, GraphRAG) to manage strict pharma brand

guidelines, compliance rules, and medical claims validation.

● Data & Search Engineering: Hands-on experience with vector databases and

enterprise search engines (e.g., Amazon OpenSearch, Sinequa Search, Adobe Search

via API) to support the Context Personalization Layer.

● DevOps & Infrastructure as Code (IaC): Strong proficiency in deploying cloud

infrastructure predictably using Terraform or cloud-specific equivalents (AWS CDK).

● Extensibility Frameworks: Mastery of the Adobe GenStudio UI Extensibility SDK

(UIX), Node.js, and Adobe Developer CLI (aio-cli) to create Add-ons that feed

context straight into Adobe's native environments if necessary.

Note: While our internal architecture is 100% AWS-native, exceptional candidates with

equivalent deep expertise in GCP or Azure who are excited to apply those patterns to an

AWS environment are highly encouraged to apply

Key Skills

Education

Not specified