Position Overview

The Forward Deployed AI (FDE) team operates at the intersection of AI research, production deployment, and customer impact. As an Agentic AI Applied Scientist, you won't sit in an isolated research lab—you will embed directly with high-growth startups, tech innovators, and AI native enterprise partners. You will design, build, and deploy custom autonomous agent architectures running on DigitalOcean’s high-performance AI infrastructure.

What You’ll Do

• Architect Production-Ready Agentic Frameworks: Design and implement sophisticated multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures capable of resolving complex, real-world customer challenges.
• Direct Customer Integration: Embed deeply with tech innovators, CTOs, and AI engineering leads to transform ambiguous business requirements into high-performance, deterministic AI/Agentic workflows.
• Synthesize Research and Deployment: Quickly prototype cutting-edge agentic frameworks—leveraging LangGraph, AutoGen, or custom execution graphs—and harden them for enterprise-scale production and stateful memory retention.
• Drive Reliability and Evaluation: Develop comprehensive reusable Evals frameworks and safety guardrails to monitor reasoning precision, execution security, latency, and cost-efficiency across agentic systems.
• Optimize the DigitalOcean Ecosystem: Serve as a strategic feedback link between our customers and core AI/ML Product teams, converting field deployment friction into foundational platform enhancements.

Travel & Collaboration Requirements: Ability to travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration. Ability to consistently overlap with North American business hours, including availability until at least noon Eastern Time, to collaborate effectively with customers, Product, Engineering, and go-to-market teams.

What You’ll Add to DigitalOcean

• 4+ years of hands-on experience in Applied AI, Machine Learning, or Data Science.
• Expertise in Multi-agent Frameworks: Proven experience building multi-agent orchestration engines, tool-use / function-calling pipelines, MCP, structured outputs, dynamic planning, and persistent memory models.
• Production Python & Systems Engineering: Strong skills in writing clean, production-ready Python (Pydantic, FastAPI, Asyncio, PyTorch).
• Customer-Facing Engineering Mindset: High empathy, crisp technical communication, and the ability to articulate complex AI trade-offs to both engineering leads and executive sponsors.
• The DO "Shark" Mentality: You think big, bold, and scrappy. You have a bias for action and a powerful sense of ownership over the customer experience.

Preferred Qualifications

• Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related technical field.
• Applied Science Experience:Experience in deep learning frameworks (like PyTorch or TensorFlow), distributed training tools as well