Forward Deployed Engineer (AI Deployment)

Role Summary

Cloudfactory is an AI enablement company where our software platform enables trusted AI at scale. Services of the platform include data preprocessing, AI model fine-tuning, inference oversight, and MLOps for AI solutions in production. For traditional enterprises that aspire to leverage AI to disrupt their industries but lack the necessary skills or capabilities, we offer forward-deployed engineering services that advise, design, prove, develop, scale, and operate AI solutions for them. You will work directly with strategic clients to design and implement scalable technical integrations, define production-ready workflows, and ensure AI systems transition from experimentation to reliable deployment. This is a hands-on engineering role with strong product and client-facing responsibilities.

Responsibilities

• AI Deployment Architecture:
  • Design and implement integrations between client systems and CloudFactory's platform
  • Architect scalable agentic AI & human-in-the-loop workflows
  • Define data ingestion, transformation, and feedback loops
  • Evaluate system bottlenecks (latency, quality, throughput)
• Product Readiness & Scale:
  • Translate proof-of-concept AI systems into scalable production workflows
  • Identify operational risks before deployment
  • Partner with Delivery teams to ensure execution feasibility
  • Improve reliability and reduce manual intervention
• Hands-on Engineering:
  • Build APIs, connectors, automation scripts, and data pipelines
  • Debug integration issues in client environments
  • Contribute to internal platform enhancements
• Field Product Intelligence:
  • Surface recurring patterns from client deployments
  • Distinguish between custom solutions and reusable features
  • Influence roadmap priorities with evidence from the field
• Technical Leadership:
  • Lead technical discovery sessions
  • Support pre-sales validation
  • Act as trusted advisor to enterprise engineering teams

Requirements

• 2+ years software engineering experience
• Strong proficiency in Python, Go
• Experience with Claude and relevant AI tools
• Experience with cloud infrastructure (AWS/GCP/Azure)
• Experience building APIs and working with distributed systems
• Experience designing data pipelines or ML infrastructure
• Experience evaluating end-to-end workflow performance
• Familiarity with AI/ML lifecycle (training → inference → evaluation → feedback)
• Ability to differentiate scalable feature opportunities from custom requests
• Strong prioritization instincts
• Ability to communicate with C-level and deeply technical stakeholders
• Comfortable running technical workshops
• Able to translate ambiguity into architecture