Senior Platform & AI Engineer

AdobeSan Jose, CA
14d

About The Position

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! Opportunity Adobe Professional Services is looking for a full-time Platform & AI Engineer with experience in building solutions/integrations using cloud technology stacks to solve digital experience solutions for Adobe’s Digital Experience enterprise customers. Customer-facing Engineers who excel at solving complex technical challenges, delighting customers, and driving agent-based automation in a collaborative environment will thrive here. If you have a strong foundation in Data Engineering, building scalable API based integrations and are passionate about architecting autonomous AI systems—including LLM agents and intelligent automation—this is your ideal next step.

Requirements

  • BS/MS degree in Computer Science or equivalent industry experience
  • AWS Certified Data Engineer - Associate
  • 7+ years of experience architecting, building, and maintaining scalable, fault-tolerant data processing workflows using AWS core/managed services (API Gateway, S3, EC2, Lambda, Glue, IAM, CloudFormation, CloudWatch, Athena, DynamoDB, EMR, Kinesis, DataSync, Transfer Family) and Python (including PySpark); strong understanding of data architecture, ETL/ELT.
  • 2+ years working with AWS AI/ML and agentic services such as SageMaker, Bedrock, Vector databases (OpenSearch, Pinecone)
  • Demonstrated experience (or significant exposure) designing, integrating, and scaling agentic AI systems—such as LLM agents, multi-agent frameworks (LangChain, LangGraph, LangSmith, MLFlow ), autonomous orchestration, or decision-making pipelines.
  • Capable of evolving data engineering solutions into intelligent, agent-based offerings.
  • Practical knowledge of CI/CD, Jenkins, CloudFormation/Terraform deploying AI agents and ML models into production, tracking autonomous workflow performance, and maintaining agentic ML operations using Docker or Kubernetes, alongside traditional pipelines.
  • Skilled in advanced data profiling, feature engineering, and crafting agentic workflows that incorporate reasoning, context, and self-directed automation.
  • Effective at bridging data engineering, AI agent development, and customer-facing requirements—working closely with Architects and Product Engineers for agentic AI and custom solutions.
  • Rapidly evolves with AI, LLM, and agentic technologies—adapting to advances in autonomous systems and demonstrating a strong growth mindset.
  • Able to turn customer requirements into sophisticated, autonomous solutions.

Nice To Haves

  • Special Consideration given for AWS Certified Machine Learning – Specialty OR AWS Certified Solutions Architect - Associate
  • Proficiency in Customer Data Platform (CDP), Data Management Platform (DMP), or Content Management System (CMS)
  • Experience & knowledge with Adobe DX Cloud solutions
  • Proficiency in AI Tools available on the Adobe DX Platform or with third-party solutions

Responsibilities

  • Collaborate with Data Architects, Solution Consultants, and Product Engineering teams to capture customer requirements and build advanced, agentic AI solutions using autonomous agents, multi-agent systems, or LLM-based orchestrations.
  • Partner with enterprise customers to develop intelligent data flows and agent-based integrations that automate, optimize, and enable autonomous decision-making within Adobe DX solutions.
  • Define, implement, and promote Data Engineering guidelines through clear documentation and reusable work. These include data models, ETL/ELT pipeline compositions, data flow diagrams, source-to-target mappings, schema definitions, data dictionaries, metadata documentation, governance and quality standards, and security and compliance protocols.
  • Develop and improve features that incorporate LLMs, AI agents, and/or multi-agent orchestration for dynamic data integration, workflow automation, and real-time business value.
  • Encourage the team to overcome technical challenges and integrate next-generation agentic AI development principles.
  • Work with Project Managers to scope, bill, and forecast time for customer solutions, demonstrating agent-based AI and automation strategies.
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