Applied AI Engineer

Boam AI; Berlin; Vor Ort; EUR 85.000 - 105.000; Vollzeit

Zusammenfassung

A leading AI technology firm in Berlin is looking for an Applied AI Engineer to take charge of the lifecycle of ML models and data pipelines. You will build and maintain systems that power enterprise-level decisions, working closely in a senior team. Ideal candidates have 3+ years in ML engineering, strong Python skills, and can develop production-ready features. Join us for a skilled role with high autonomy, meaningful equity options, and the chance to make a real impact from day one.

Leistungen

Top-tier compensation

Meaningful equity upside

High autonomy and clear ownership

Qualifikationen

• 3+ years of ML engineering experience working on production systems.
• Strong Python skills and ownership of end-to-end ML pipelines.
• Comfortable with data preprocessing, feature engineering, and evaluation at scale.

Aufgaben

• Own the ML model lifecycle from training and evaluation to deployment.
• Build and maintain ML & agentic data pipelines for training, inference, and monitoring.
• Develop production-ready agentic and large-language-model–driven features.

Kenntnisse

ML engineering experience

Python skills

Data preprocessing

Familiarity with LLMs

Experience with ML tooling

Problem-solving skills

Tools

PyTorch

TensorFlow

XGBoost

Jobbeschreibung

Ship production ML and agentic AI powering market leaders worldwide Boam AI builds managed data solutions that transform messy, unstructured signals from public, private, and proprietary sources into structured, reliable, and always up-to-date intelligence on millions of SMBs and enterprises worldwide. These agentic systems power CRMs, data warehouses, AI products, and mission-critical decisions across the enterprise.

As an Applied AI Engineer, you will own the lifecycle of the models and agents that power our product. You will build and maintain ML data pipelines, develop production-ready LLM- and agent-driven features, and work closely with backend engineers to integrate models into real systems. This is a role for someone who has shipped ML to production, wants real ownership over models and pipelines, and is excited to work on a small, senior team where AI is at the core of the product.

What You’ll Do

• Own the ML model lifecycle from training and evaluation to deployment
• Build and maintain ML & agentic data pipelines for training, inference, and monitoring
• Develop production-ready agentic and large-language-model–driven features
• Integrate models into production systems in close collaboration with backend engineers
• Implement experiment tracking, model CI/CD, and automated retraining
• Improve performance, reliability, and observability of ML and AI systems in production
• Work with product and data teams to turn ambiguous problems into concrete ML/AI solutions
• Use next-gen AI tools to improve iteration speed and model quality

You Might Be a Fit If...

• 3+ years of ML engineering experience working on production systems
• Strong Python skills and hands-on ownership of end-to-end ML pipelines or agentic systems
• Comfortable with data preprocessing, feature engineering, and evaluation at scale
• Familiarity with LLMs or modern foundation models
• Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers)
• Comfortable working across APIs, data stores, and infrastructure, not just notebooks
• Bias to ship, measure, and refine rather than chase perfect offline metrics
• Motivated by solving real customer problems and seeing models used in the wild
• Thrive without heavy process, QA buffers, or endless safeguards – you own what you ship

Why Boam AI

• Join a no-politics, high-trust, low-ego, and high-talent team
• Work on mission-critical ML/AI systems used by top-tier enterprise customers
• Work directly with founders, the Head of Engineering, and senior engineers on problems that matter
• High autonomy, real impact, and clear ownership from day one
• Operate at the intersection of AI, data infrastructure, and enterprise workflows
• Top-tier compensation with meaningful equity upside
• Help shape the ML/AI platform, patterns, and practices you can be proud of

Our Hiring Process

1. Intro Call: A short conversation to learn more about you, share context on Boam AI and the ML/AI role, and answer initial questions
2. Deep Dive: Walk us through past ML/AI work, systems you have built, and how you think about complex, ambiguous modeling and production challenges
3. Work Sample: Solve a real Boam-style ML/AI challenge that shows your modeling approach, pipeline thinking, and execution muscle
4. Founder / Leadership Conversation: Candid discussion with our founder and Head of Engineering on ambition, values, ownership, and how you would help us scale our ML and agentic systems