As an AI/ML Engineer, you will play a key role in developing and implementing machine learning and AI-powered products, contributing to the growth and success of our customers and organization.

Research and Development:

• Stay updated with the latest advancements in machine learning and AI technologies, tools, and methodologies.

• Conduct research to identify and explore potential applications of machine learning in the company's products or services.

• Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to conceptualize and design innovative machine learning solutions.

Machine Learning Model Development:

• Develop and implement machine learning models and algorithms to solve complex business problems.

• Design and optimize data pipelines, feature engineering processes, and model training techniques.

• Apply statistical analysis and evaluation methods to ensure the accuracy, efficiency, and reliability of the models.

• Regularly monitor and maintain deployed models, ensuring their continuous performance and adapting them to changing data patterns.

Model Deployment and Integration:

• Collaborate with software engineering teams to deploy machine learning models into production environments.

• Develop and maintain APIs or other interfaces to integrate machine learning capabilities into existing systems or applications.

• Collaborate on monitoring and maintenance procedures to ensure model stability, performance, and scalability.

Documentation and Reporting:

• Prepare comprehensive documentation, including technical specifications, user guides, and best practice guidelines for machine learning models and systems.

• Present findings, insights, and project updates to both technical and non-technical stakeholders in a clear and concise manner.

• Continuously track and report on the progress of machine learning projects, ensuring alignment with established goals and timelines.

• 2+ years of experience working in related roles (ML engineer, AI engineer)

• Experience in the complete end-to-end process and life cycle of model development: From data acquisition and preparation to the deployment and go-live phase.

• Expert proficiency in Python and building APIs using FastAPI and common data science toolkits: NumPy, pandas, scikit-learn, etc. 

• Expertise in NLP and the fields of topic modeling, document classification, named entity recognition and recommender systems.

• Expertise in transformer-based large language models (BERT, GPT, Llama, etc.)

• Advanced knowledge of prompt engineering techniques, e.g. zero / few shot classification, Chain-Of-Thought, etc.

• Experience in fine-tuning open-source language models on both labeled (supervised) and unlabelled (unsupervised) domain-specific data using various techniques like LoRa and PEFT / ReFT.

• Experience in validating, evaluating and benchmarking LLM performance on specific tasks and a deep understanding of deploying LLMs in real-world scenarios.

• Experience with LangChain with implementation of RAG.

• Extensive experience in at least one of the following Machine Learning frameworks: PyTorch, TensorFlow, JAX.

• Solid understanding of statistical concepts and methodologies, with the ability to apply them effectively in data analysis and model development.

• Experience with data modeling, data warehousing, ETL and data pipelines is a plus.

• Experience with cloud computing (AWS, GCP, Azure).

• Flat hierarchies, a driven international, diverse, and open-minded team - fun to work and collaborate with. 

• A transparent feedback culture.

• Plenty of room to contribute your ideas and successfully participate in our growth. 

• 30 days annual vacation, plus an attractive annual salary. 

• Open, modern office in Kreuzberg.

• 3 days office days - 2 days home office policy, plus 10 days per year when you can flexibly work remotely.

• Screening call with our recruiter (20 minutes virtual) 

• Hiring manager interview (60 minutes virtual)

• Take-home ML design task (up to 4 hours self-directed work)

• Design task review (60 mins)

• Meet the team, meet the founders, join the company lunch (3 hours onsite)

• Reference checks