Senior Forward Deployed AI Engineer, Enterprise

Scale AI Scale AI · Data AI · New York, NY +1 · Enterprise Engineering

Senior Forward Deployed AI Engineer for Scale AI's Enterprise team, acting as a technical bridge to strategic customers. Responsibilities include understanding customer challenges, architecting custom AI solutions, ensuring successful deployment and adoption of AI systems, developing production-grade AI agents and multi-agent systems, implementing evaluation frameworks, prompt engineering, RAG, fine-tuning, and collaborating with customer and internal teams. Requires strong software engineering, Python, ML/AI frameworks, and cloud platform experience.

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. Create sophisticated prompt engineering strategies optimized for customer-specific domains and data

Skills

Required

  • 4+ years of software engineering experience
  • Production Python expertise
  • Experience with modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • Experience with modern data infrastructure
  • Strong problem-solving skills
  • 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)
  • Experience with CI/CD pipelines
  • Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
  • 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 AI applications
  • production deployment
  • production Python expertise

Other signals

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