Lead AI Architect, Salesforce Service Cloud & AWS

POSITION SUMMARY We are seeking a visionary Lead AI Architect to lead the design and implementation of next-generation AI solutions by bridging our Salesforce CRM ecosystem with AWS cloud infrastructure. In this strategic role, you will be responsible for architecting scalable Generative AI applications that enhance customer engagement, automate complex workflows, and unlock predictive insights. You will serve as the technical authority on how Salesforce Data Cloud, Einstein Trust Layer, and Amazon Bedrock interact, ensuring our AI solutions are secure, scalable, and impactful. You will move beyond simple integrations to build robust "Bring Your Own Model" (BYOM) architectures. PRIMARY RESPONSIBILITIES AI Strategy & Architecture (40%) Define the end-to-end architecture for Generative AI solutions, integrating Salesforce Data Cloud with Amazon Bedrock and SageMaker . Design "Bring Your Own Model" (BYOM) patterns, allowing Salesforce to securely access Foundational Models (FMs) hosted on AWS (e.g., Claude, Titan, Llama 2) via Bedrock. Architect the data ingestion and grounding strategies (RAG - Retrieval Augmented Generation) to ensure AI models have access to real-time, unified customer profiles from Data Cloud. Establish the governance framework for AI, ensuring compliance with the Einstein Trust Layer regarding data privacy, zero-data retention policies, and PII masking. Implementation & Development (30%) Lead the technical implementation of Model Builder and Prompt Builder within Salesforce, connecting them to external AWS endpoints. Configure Amazon Bedrock agents and knowledge bases to execute complex tasks and retrieve proprietary data for Salesforce users. Develop serverless middleware using AWS Lambda and API Gateway to facilitate low-latency communication between Salesforce Flow/Apex and AWS inference endpoints. Oversee the fine-tuning of models on AWS SageMaker when out-of-the-box Bedrock models require domain-specific adaptation. Cross-Functional Collaboration (20%) Partner with Data Engineers to ensure Salesforce Data Cloud is correctly ingesting, harmonizing, and activating data streams from S3, Redshift, and other sources. Collaborate with Salesforce Administrators and Developers to embed AI outputs directly into the flow of work (Service Console, Sales Cloud LWC, etc.). Act as the subject matter expert for Product Managers, translating "AI buzzwords" into viable technical roadmaps and realistic deliverables. Optimization & Innovation (10%) Monitor LLM performance, latency, and token costs; implement caching strategies and model selection logic to optimize spend. Stay ahead of the rapid evolution in the Salesforce/AWS partnership, evaluating new features like Zero Copy Data Sharing and Vector Databases . QUALIFICATIONS Required Technical Skills: AWS AI/ML Stack: Deep expertise in Amazon Bedrock is mandatory. Experience configuring Bedrock Knowledge Bases, Agents, and Guardrails. Proficiency with AWS Lambda, API Gateway, and IAM for secure cross-cloud access. Salesforce AI Stack: Strong working knowledge of Salesforce Data Cloud , Einstein 1 Platform , and the setup of Model Builder/Prompt Builder. Architecture: Experience designing RAG (Retrieval Augmented Generation) architectures and Vector Database integrations (e.g., OpenSearch Serverless or Pinecone). Integration: Expert-level knowledge of REST/gRPC APIs, OAuth flows, and named credentials for secure Salesforce-to-AWS connectivity. Programming: Proficiency in Python (for AWS Lambda/Boto3) and familiarity with Apex (for Salesforce triggers/callouts). Experience: 8+ years of experience in Enterprise Architecture, Cloud Engineering, or Data Science. 3+ years specifically focused on AI/ML solutioning or Data Engineering. Proven track record of deploying at least one Generative AI solution into production (not just POCs). Experience implementing "Zero Copy" architecture between Salesforce and Data Warehouses (Snowflake/Redshift) is a major plus. Certifications (Pr