Job Title: Tech Lead – Agentic AI / Content Supply Chain
Location: SFO, CA – Onsite, Hybrid
Employment Type: Contract (C2C)
Experience: 12 to 15 years
Job 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.
Required Skills & Experience
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 VertexAI, 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/StepFunctions, Google Cloud Functions, or Azure Functions) and secure API patterns(REST, GraphQL).
· Advanced RAG & Semantic Layers: Capability to design graph-based knowledgeretrieval 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 andenterprise search engines (e.g., Amazon OpenSearch, Sinequa Search, Adobe Search via API) to support the Context Personalization Layer.
· 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.
Primary Skill Set
Key Responsibilities
Enterprise Agentic AI Architecture & Master Orchestration
· System Integration & Orchestration: Design and implement the Master AgenticOrchestration layer using cloud-native tools (e.g., AWS Step Functions/Bedrock Agents,GCP Vertex AI, or Azure OpenAI/Semantic Kernel) to interface seamlessly with adjacentlegacy systems.
· End-to-End Content Supply Chain Automation: Map and build multi-agent workflowsthat securely source data from Adobe technologies, utilize foundation models togenerate compliant text, extract metadata from digital assets, and push assets intodownstream API-driven consumption layers.
· Guardrails & Compliance Execution: Implement strict operational boundaries using AIguardrails, content moderation APIs, and serverless computing to guarantee thatAI-generated text and visual components adhere to strict brand, safety, and regulatoryguidelines.
Testing, Quality Assurance & Message Testing
· Agent Logic Validation: Validate the state management and decision-making logic ofautonomous AI agents using robust ML tracking to ensure automated outputsconsistently meet business rules.
· Simulation & Testing Frameworks: Architect and deploy a Digital Twin OutcomeSimulator for message testing and an Intelligent A/B Testing Workspace leveragingcontainerized microservices and clean data rooms to safely model and validate contentefficacy 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.
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 datatools and vector search engines to power the Insights Engine Ingestion & ContextPersonalization Layer.
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.
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
Thanks & Regards,
Harshith Reddy
harshith0728@gmail.com
