Forward Deployed AI Engineering Manager, Enterprise

Scale AI Scale AI · Data AI · San Francisco, CA · Enterprise Engineering

This role is for an Engineering Manager on the Enterprise team at Scale AI, focusing on being a technical bridge between Scale AI's AI capabilities and strategic enterprise customers. Responsibilities include understanding customer challenges, leading a team to architect and deploy AI solutions (specifically AI agents and integrations), prompt engineering, and RAG systems. The role requires strong software engineering and management experience, Python expertise, and cloud platform familiarity, with a focus on customer-facing AI deployments.

What you'd actually do

  1. Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
  2. Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
  3. Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
  4. Architect multi-agent systems that orchestrate between different models, tools, and data sources
  5. Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts

Skills

Required

  • 5+ years of software engineering experience
  • 2+ yrs of Management experience
  • strong fundamentals in data structures, algorithms, and system design
  • Production Python expertise
  • experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • modern data infrastructure
  • Strong problem-solving skills
  • ability to navigate ambiguous requirements and rapidly iterate toward solutions
  • Excellent communication skills

Nice to have

  • Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
  • Experience building and deploying AI agents or autonomous systems in production
  • Knowledge of vector databases and semantic search systems
  • Contributions to open-source AI/ML projects
  • Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
  • Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
  • Previous work in a devops, platform, or infra role
  • Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)
  • Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
  • Domain expertise in verticals like finance, healthcare, government, or manufacturing
  • Experience with technical enablement or teaching programs

What the JD emphasized

  • production AI applications
  • production environments
  • production-grade AI agents
  • production environments
  • production environments

Other signals

  • customer integration
  • AI agent development
  • prompt engineering
  • deployment in production environments