Machine Learning Engineer / AI Engineer

Machine Learning Engineer / AI Engineer

Education: MSc or PhD in Natural Language Processing, Data Science, or related field (no pure Computer Science, unless there was a strong focus on data science, e.g., by having done related projects and internships)

Background: At least 2-3 years of working experience, 4+ would be better. Work in academia only counts as experience if the applicant has completed a PhD

Proficient with: Machine Learning in general (refining data, developing, training, and testing feature-based and more modern GenAI/transformer-based models, integrating models into production [last point very important]), multi-modal GenAI (esp. text and images), software patterns like RAG with hybrid search and more common software engineering knowledge like objected-oriented principles, experiences with finetuning LLMs, experienced with agent-based systems. Salesforce/ServiceNow experience is a plus. Experience or background with/in Chemistry is a plus.

Techstack: PyTorch/Tensorflow, Huggingface, Transformers/SentenceTransformers, LangChain, LangGraph, sklearn, numpy, Pandas, SQL/NoSQL DBs including Vector DBs, Docker/Kubernetes, MLFlow, FastAPI, GraphQL, Azure, AWS, git

ML Eng. (Multimodal GenAI) ML Eng. (Multimo... by Juan Buero Viana (Unverified)Juan Buero Viana (Unverified)1:28 PM

• ML Eng. (Multimodal GenAI)
• ML Eng. (Multimodal GenAI, including videos)
• Computer Vision Experts, proficient in visual 2D/3D data modeling, proficient in generating 3D models from 2D drawings
• ML Eng. Visual Data 2D/3D Data
• ML Eng. NLP (GenAI, RAG, building/finetuning LLMs) + SalesForce expertise
• ML Eng. NLP (GenAI)
• ML Eng. Proficient with Knowledge Graphs and Graph Databases
• Software Eng. with Knowledge Graphs and Graph Databases
• UI/UX designers experienced with images and videos
• Data Scientist (Statistician for analyzing factors like impact, best publishing dates, etc.)
• ML Eng. (Optimization)
• ML Eng. NLP (multimodal GenAI, Agents, chemical language/chemical formulas, finetuning LLMs)
• ML Eng.Forecasting (Neural Forecasting Models)
• ML Eng. Optimization (optimizing financial regression predictions)
• Data Engineer (IoT, for reading warehouse data)