Data Scientist – AI, Machine Learning

Role Overview

• Design and train supervised and unsupervised models
• Develop NLP and LLM solutions
• Perform fine-tuning of foundational models
• Implement RAG (Retrieval-Augmented Generation) architectures
• Create training, validation, and inference pipelines
• Evaluate model performance (accuracy, F1, ROC, BLEU, etc.)
• Implement monitoring for drift and performance degradation
• Collaborate with engineering teams to deploy models to production
• Manage computational costs for training and inference

Requirements

• Languages
• Python (primary)
• SQL (analysis and feature engineering)

Machine Learning & Deep Learning

• PyTorch
• TensorFlow
• Scikit-learn
• XGBoost / LightGBM

LLM & GenAI

• OpenAI APIs
• Open-source models via Hugging Face
• Transformers
• Fine-tuning (LoRA, PEFT)
• Advanced prompt engineering
• Embeddings and vector similarity
• RAG architectures

MLOps

• MLflow
• Weights & Biases
• Docker
• CI/CD for models
• Model monitoring
• Dataset versioning

Infrastructure

• GPU environments
• Kubernetes
• Cloud (preferably one of the following):
• Amazon Web Services
• Google Cloud
• Microsoft Azure

Hard Skills

• Applied statistics and probability
• Predictive modeling
• NLP
• Model evaluation and validation
• Advanced feature engineering
• Fine-tuning LLMs
• Building ML pipelines
• MLOps and model deployment
• Performance and cost optimization

Soft Skills

• Deep analytical thinking
• Experimental mindset (constant hypothesis testing)
• Clear technical communication
• Ability to translate business problems into mathematical models
• Technical autonomy
• Sense of responsibility for model impact

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• Kubernetes
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Position also open to candidates with disabilities (PwD)