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
