Senior AI Engineer

About the Role

Senior AI Engineer

Key Responsibilities

• Technical Leadership:

  Direct a small sub-team of engineers and scientists, overseeing project delivery, code reviews, and technical mentorship in an Agile environment.

• LLM & RAG Implementation:

  Architect and deploy advanced RAG pipelines and LLM-based solutions, including instruction fine-tuning and prompt engineering.

• Advanced Modeling:

  Implement Deep Learning and NLP models to extract insights and structure clinical data from vast, unstructured datasets.

• System Architecture:

  Design high-performance data pipelines and manage large-scale NoSQL/Graph databases (e.g., MongoDB, Elastic Search, Neo4J).

• Strategic Collaboration:

  Partner with medical experts and commercial teams to integrate AI into healthcare digitization and contribute to company IP (patents/whitepapers).

Requirements

• Experience:

  6+ years in Data Science or Data Engineering with experience leading technical sub-teams.

• AI Expertise:

  Deep proficiency in

  LLMs, RAG architectures

  , and Deep Learning frameworks (

  PyTorch or TensorFlow

  ).

• Python Mastery:

  Superior coding skills with deep knowledge of data structures, algorithms, and software design principles.

• Data Infrastructure:

  Hands-on experience with

  NoSQL/Graph DBs

  , async/parallel processing, and information retrieval.

• Engineering Rigor:

  Experience with software development best practices, unit testing, and deployment using

  Docker/Kubernetes

  .

• Education:

  Degree (Master/PhD preferred) in Computer Science, Mathematics, Physics, or Engineering.

• Language:

  Professional proficiency in both English and German is compulsory.

Nice-to-Have

• Experience with

  Scrapy

  and distributed web crawling at scale.
• Familiarity with Big Data tools like

  Hadoop, Spark, or Kafka

  .
• Previous exposure to the

  Atlassian suite (JIRA)

  and Agile methodologies