Data & ML Engineer

About Us:
MULTIPLAI GmbH is a leading consulting firm specializing in data and AI strategy. We empower organizations to harness the power of data and AI strategically, guiding them toward transformative success. Our team of experienced senior leaders brings a wealth of knowledge, strategic insight, and hands-on expertise to our clients.

Position Overview:
We are seeking a hands-on Data & ML Engineer with junior architect capabilities. You will design and build scalable data pipelines, develop and deploy machine learning applications, and contribute to the architecture of modern data platforms. This role combines deep technical execution with an eye for design, perfect for engineers who want to grow into architectural responsibilities. You will work across client teams, turning complex data challenges into real-world solutions, and actively shaping production-grade AI systems.

At MULTIPLAI, we don't just build solutions - we build capabilities. We are the partner of choice for enterprises navigating their AI journey, combining strategic clarity with technical depth. Join a team of experienced practitioners who value craftsmanship, autonomy, and impact. Whether you're deploying pipelines, debugging models, or contributing to architecture designs, you'll be shaping the future of applied AI and data engineering.

Key Responsibilities:

• Design and implement scalable data pipelines and feature stores for ML use cases.

• Deploy and monitor ML models in production environments, with attention to stability and performance.

• Work across cloud platforms (Azure, AWS, GCP) to deliver modern, modular data and ML solutions.

• Contribute to architecture decisions for data and ML workflows (batch, streaming, and real-time).

• Collaborate with consultants, architects, and client teams to understand business problems and translate them into technical designs.

• Apply MLOps and DataOps principles to streamline delivery and reduce technical debt.

• Curiosity and a growth mindset — willingness to absorb new knowledge at high pace.

• Proven experience in data engineering and ML implementation on cloud-native platforms.

• Proficiency in Python and SQL, with experience in distributed computing (Spark, Dask, or similar).

• Hands-on experience with ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow).

• Familiarity with LLMs and RAG frameworks (LangChain, LangGraph, LlamaIndex) is a plus.

• Good understanding of data modeling, orchestration, and versioning (e.g., dbt, Airflow).

• Experience with cloud-native ML tooling (e.g., Azure ML, Vertex AI, SageMaker).

• Comfortable working in fast-paced, client-facing environments.

• Previous experience in professional services or consulting is advantageous.

• The ideal candidate additionally would have experience with knowledge graphs and ontology management.