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Senior Forward Deployed AI/ML Engineer

Developrec — Germany · Posted ~2 days ago

Senior Hybrid €100000-€150000

Skills

Reinforcement Learning Python Machine Learning Systems Multi-Agent Systems RAG Production ML ML System Design Stakeholder Communication Enterprise Integration PPO PyTorch JAX TensorFlow MLOps RLOps

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Summary

A rapidly growing investment-focused organization is seeking a senior engineer to embed with operating businesses and deploy custom reinforcement-learning models, autonomous agents, and retrieval-augmented systems. You will translate complex workflows into formal ML frameworks, build scalable Python integrations, and balance advanced AI capabilities with production requirements for latency, cost, and reliability. Deep practical RL expertise and strong communication skills are essential.

Highlights

High-impact work deploying advanced AI and reinforcement-learning systems into real operational environments, with strong compensation and direct exposure to complex business transformation challenges.

Description

Senior Forward Deployed Engineer (ML/AI & RL) Location: Berlin, Germany (Hybrid – 2 days on-site) Salary: €100,000 – €150,000 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.