Tech Lead Agentic AI / Content Supply

Role: Tech Lead  Agentic AI / Content Supply

Long Term Contract

This role has to be in SFO and working from office for atleast 3 days a week in South SFO.

Must have Pharma Domain Experience

Need Senior candidate 

Resource already hands on experience in AI/ML/ LLM Orchestration in Pharma companies will be more suited for this role.

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.

Key Responsibilities

1. 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,

Google Cloud Platform 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.

2. 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.

3. 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, Google Cloud Platform, 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, Google Cloud Platform 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 Google Cloud Platform or Azure who are excited to apply those patterns to an

AWS environment are highly encouraged to apply