Senior Forward Deployed Engineer

develop; Berlin; Hybrid; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Develop in Berlin is seeking a Senior Forward Deployed Engineer to embed with portfolio companies, architect and deploy RL models and autonomous agentic systems addressing real-world challenges.

You will build production-grade RL pipelines, translate business logic into RL frameworks, and write scalable Python to integrate ML/RL pipelines with enterprise infrastructure. The role requires delivering measurable operational impact with strong communication and collaboration across teams.

Qualifikationen

• Deep practical RL experience (PPO, policy gradients, multi-agent RL) and production ML system design.
• Expert Python skills (PyTorch/JAX/TensorFlow); familiarity with MLOps/RLOps and full-stack integration.
• Strong communication and track record delivering measurable operational impact.

Aufgaben

• Deploy production-grade RL models, multi-agent workflows, and RAG pipelines into legacy environments.
• Translate messy operational workflows into formal RL state/action/reward spaces.
• Write scalable Python to integrate ML/RL pipelines with existing enterprise infra.
• Balance frontier AI with production constraints such as latency, cost, and reliability.

Kenntnisse

Reinforcement Learning

Python

MLOps

RLOps

Communication

Tools

PyTorch

JAX

TensorFlow

Jobbeschreibung

Location: Berlin, Germany (Hybrid - 2 days on-site)

The Role

A high-growth investment firm transforming legacy acquisitions via advanced AI is seeking a Senior Forward Deployed Engineer. You will embed with portfolio companies to architect and deploy custom Reinforcement Learning (RL) models and autonomous agentic systems that solve complex, real-world operational challenges.

Core Responsibilities

• Deploy RL & AI: Build and ship production-grade Reinforcement Learning models, multi-agent workflows, and RAG pipelines into legacy environments.
• Translate Business Logic: Convert messy operational workflows into formalized RL frameworks (defining state, action, and reward spaces).
• Hands-On Engineering: Write scalable Python code to integrate custom ML/RL pipelines with existing enterprise infrastructure.
• Pragmatic Delivery: Balance frontier AI capabilities with strict production constraints (latency, cost, and reliability).

Key Requirements

• Core Expertise: Deep practical experience in Reinforcement Learning (e.g., PPO, policy gradients, multi-agent RL) and production-grade ML system design.
• Engineering Rigor: Expert Python skills (PyTorch/JAX/TensorFlow); familiarity with MLOps/RLOps and full-stack integration is a plus.
• Forward Deployed Mindset: Strong communication skills with a proven track record of delivering measurable operational impact.

The Process

• 4-Stage Interview: Focused on ML/RL architecture, technical execution, and stakeholder alignment.