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Role: AI/ML Architect Location: Fremont, CA Duration: Fulltime
Key Responsibilities
System Design: Design end-to-end AI/ML architectures, including data ingestion, feature stores, model training pipelines, and real-time inference services.
Infrastructure & MLOps: Establish robust MLOps practices (CI/CD for ML) to automate the deployment, monitoring, and retraining of models.
Integration Specialist: Work closely with the Salesforce and Digital Engineering teams to embed AI capabilities (like Agentforce or custom LLMs) into existing business workflows.
Technical Governance: Evaluate and select third-party AI tools, frameworks, and cloud services (AWS, Azure, Google Cloud Platform) to ensure a future-proof tech stack.
Performance Optimization: Conduct architectural reviews to optimize model latency, throughput, and cloud infrastructure costs.
Technical Requirements
Engineering Excellence: 7 years of experience in software engineering and data architecture, with at least 3 years focused specifically on ML systems.
Cloud Architecture: Deep expertise in architecting on AWS (SageMaker), Azure (Azure ML), or Databricks.
Generative AI Stack: Proficiency in designing RAG (Retrieval-Augmented Generation) architectures, vector database selection (Pinecone, Weaviate, Milvus), and LLM orchestration (LangChain, LlamaIndex).
Data Mastery: Strong experience with Spark, Flink, or Snowflake for large-scale data processing.
Security & Compliance: Knowledge of designing systems that adhere to SOC2, GDPR, or HIPAA, especially regarding data residency and model privacy.
Soft Skills
Problem Solver: The ability to take a vague business problem and decompose it into a technical blueprint.
Mentor: A passion for conducting code reviews and guiding ML Engineers on best practices in system design.
Role: AI/ML Architect Location: Fremont, CA Duration: Fulltime
Key Responsibilities
System Design: Design end-to-end AI/ML architectures, including data ingestion, feature stores, model training pipelines, and real-time inference services.
Infrastructure & MLOps: Establish robust MLOps practices (CI/CD for ML) to automate the deployment, monitoring, and retraining of models.
Integration Specialist: Work closely with the Salesforce and Digital Engineering teams to embed AI capabilities (like Agentforce or custom LLMs) into existing business workflows.
Technical Governance: Evaluate and select third-party AI tools, frameworks, and cloud services (AWS, Azure, Google Cloud Platform) to ensure a future-proof tech stack.
Performance Optimization: Conduct architectural reviews to optimize model latency, throughput, and cloud infrastructure costs.
Technical Requirements
Engineering Excellence: 7 years of experience in software engineering and data architecture, with at least 3 years focused specifically on ML systems.
Cloud Architecture: Deep expertise in architecting on AWS (SageMaker), Azure (Azure ML), or Databricks.
Generative AI Stack: Proficiency in designing RAG (Retrieval-Augmented Generation) architectures, vector database selection (Pinecone, Weaviate, Milvus), and LLM orchestration (LangChain, LlamaIndex).
Data Mastery: Strong experience with Spark, Flink, or Snowflake for large-scale data processing.
Security & Compliance: Knowledge of designing systems that adhere to SOC2, GDPR, or HIPAA, especially regarding data residency and model privacy.
Soft Skills
Problem Solver: The ability to take a vague business problem and decompose it into a technical blueprint.
Mentor: A passion for conducting code reviews and guiding ML Engineers on best practices in system design.