Lead AI/ML Engineer

develop; Berlin; Hybrid; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

develop in Berlin is seeking a Senior Staff Engineer to design and deploy AI systems that drive measurable business impact. You will build end-to-end AI solutions (LLMs, agents, and RAG) to automate complex workflows and improve operational efficiency.

You will work hands-on with Python, collaborating across teams to balance accuracy, cost, and user experience while meeting production requirements. This role follows a pragmatic engineering approach with a strong emphasis on delivery.

Qualifikationen

• Deep experience in production AI design, including agentic workflows and structured outputs.
• Strong track record of shipping software with tangible business outcomes.
• Expert Python skills; familiarity with Go, TypeScript, and modern ML infrastructure is advantageous.

Aufgaben

• Design & Deploy: Build end-to-end AI systems (LLMs, agents, and RAG) to automate complex operational workflows.
• Solve Real-World Problems: Convert legacy data into production-grade automation to increase throughput and reduce costs.
• Pragmatic Engineering: Balance frontier AI capabilities with production requirements (latency, cost, reliability).
• Hands-on Development: Work with the full stack using Python to ship scalable, AI-powered solutions.

Kenntnisse

Python

Go

TypeScript

LLMs

RAG pipelines

Tools

ML infrastructure

Jobbeschreibung

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

The Opportunity

My client is a high-growth investment and transformation firm that acquires established businesses and modernises them through advanced AI integration. By leveraging technology to overhaul legacy operations, they turn operational complexity into high-margin product value. They are seeking a Senior Staff Engineer to build and deploy AI systems that drive measurable business impact.

Core Responsibilities

• Design & Deploy: Build end-to-end AI systems (LLMs, agents, and RAG) to automate complex operational workflows.


• Solve Real-World Problems: Partner with internal teams to convert messy, legacy data into production-grade automation that increases throughput and reduces costs.


• Pragmatic Engineering: Balance frontier AI capabilities with speed, simplicity, and strict production requirements (latency, cost, and reliability).


• Hands-on Development: Work closely with the full stack, utilizing Python to ship scalable, AI-powered solutions.

Key Requirements

• AI-Native Practice: Deep experience in production AI design, including agentic workflows, model evaluation, and structured outputs.


• Proven Impact: A strong track record of shipping software that delivered tangible business or operational outcomes.


• Technical Rigor: Expert Python skills; familiarity with Go, TypeScript, and modern vector/ML infrastructure is advantageous.


• Product Mindset: Ability to balance technical depth with pragmatic trade-offs regarding accuracy, cost, and user experience.

The Process

• 4-Stage Interview: A streamlined process focusing on technical ability, product intuition, and team alignment.