Forward Deployed AI Engineer, Enterprise

Role overview

Responsibilities

• check_circle Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• check_circle Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• check_circle Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• check_circle Deploy and configure AI models and agents within customer security and compliance boundaries
• check_circle Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• check_circle Architect multi-agent systems that orchestrate between different models, tools, and data sources
• check_circle Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• check_circle Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement
• check_circle Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• check_circle Build and maintain prompt libraries, templates, and best practices for customer use cases
• check_circle Conduct systematic prompt experimentation and A/B testing to improve model outputs
• check_circle Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate
• check_circle Serve as the primary technical point of contact for strategic enterprise accounts
• check_circle Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
• check_circle Provide technical training and knowledge transfer to customer teams
• check_circle Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• check_circle Document technical architectures, integration patterns, and best practices
• check_circle Debug complex technical issues across the entire stack, from data pipelines to model outputs
• check_circle Rapidly prototype solutions to unblock customers and prove out new use cases
• check_circle Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• check_circle Identify opportunities for productization based on common customer patterns

Basic qualifications

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

Preferred qualifications

• 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

About the company