Developer AI Vibe Coding & Engineering

Excelloit; Deutschland; Remote; EUR 90,000 - 150,000; Full time

Job summary

Excelloit is seeking an experienced ML Engineer to design, train, evaluate, and ship ML and deep learning models across classification, regression, vision, NLP, and time series tasks.

You will build LLM-powered apps, create RAG systems, and deploy services on Azure with Docker and Kubernetes, collaborating with product and security teams to ensure quality and compliance.

Qualifications

• Bachelor’s or Master’s degree in CS/ML/DS or equivalent experience.
• 4+ years software engineering, with 2+ years ML/AI in production.
• Strong Python and ML libraries: PyTorch/TensorFlow, scikit-learn, pandas, NumPy.
• Hands-on deploying LLM-based applications with major providers.
• RAG and embeddings experience with at least one production vector store.
• Proficiency with AI coding tools such as Claude Code and Cursor; solid coding fundamentals.
• Solid Git, branching strategies, and PR-based reviews.
• CI/CD pipelines experience (Azure DevOps, GitHub Actions).
• Azure deployment and Docker-based services (AKS, App Service).
• Excellent written and verbal communication; ability to explain modeling decisions.
• Experience thriving in fully remote, globally distributed teams.

Responsibilities

• Design, train, evaluate, and ship ML/DL models across various domains.
• Design and implement LLM-powered applications and RAG systems.
• Develop agentic workflows with tool use and orchestration; implement guardrails.
• Apply prompt engineering, fine-tuning, and evaluation metrics.
• Build and maintain data and feature pipelines on Azure with reproducibility.
• Productionize MLOps with versioning, retraining, drift detection, and rollback.

Skills

Strong communication

Remote teamwork

Problem solving

Model evaluation

Education

Bachelor’s or Master’s in CS/ML/DS

Tools

Python

PyTorch

TensorFlow

scikit-learn

pandas

NumPy

Azure Kubernetes Service (AKS)

Docker

Git

GitHub Actions

Job description

• Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.
• Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).
• Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.
• Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.
• Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.
• Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility.
• Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies.
• Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding — choosing the right approach for each task and reviewing AI-generated code with rigor.
• Manage source code in Git using a clean branching strategy and pull-request-based review.
• Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services.
• Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep).
• Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time.
• Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts.
• Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job.
• Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems.
• Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first‑class concerns.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field — or equivalent practical experience.
• 4 + years of professional software engineering experience, with at least 2 years building and shipping ML or AI systems in production.
• Strong proficiency in Python and core ML libraries (PyTorch and/or TensorFlow, scikit‑learn, pandas, NumPy).
• Hands‑on experience deploying LLM‑based applications using major model providers (Anthropic Claude, Azure OpenAI, OpenAI, or open‑source models).
• Practical RAG and embeddings experience, including at least one production vector store.
• Demonstrated proficiency with AI coding tools such as Claude Code and Cursor, balanced with strong manual coding fundamentals.
• Solid command of Git, branching strategies, and pull‑request‑based code review.
• Hands‑on experience building and operating CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for production workloads.
• Hands‑on experience deploying services on Azure (AKS, App Service, Functions, Azure AI/ML services, or similar) and with containerization (Docker).
• Strong written and verbal communication; able to explain modeling decisions and trade‑offs to non‑technical stakeholders.
• Proven ability to thrive in a fully remote, globally distributed team.