Data Scientist, Applied AI - Remote

Bachelor’s or Master’s in Computer Science, Data Science or related field 5+ years of professional experience with Python in production environments Solid background in machine learning & deep learning (CNNs, Transformers, LLMs) Hands-on experience with PyTorch or similar frameworks Proven track record deploying ML solutions Expert in pandas, NumPy and scikit-learn Familiarity with Agile/Scrum practices and tooling (JIRA, Confluence) Strong foundation in statistics and experimental design Excellent written and spoken English Experience with cloud platforms (AWS, GCP, or Azure) and their AI-specific services Familiarity with big-data ecosystems (Spark, Hadoop) Practice in CI/CD & container orchestration (Jenkins/GitLab CI, Docker, Kubernetes) Exposure to MLOps/LLMOps tools (MLflow, Kubeflow, TFX) Experience with Large Language Models, Generative AI, prompt engineering, and RAG pipelines Hands-on experience with vector databases Experience building AI Agents and using frameworks like Hugging Face Transformers, LangChain or LangGraph Documentation skills using PlantUML or similar

Design, train, and validate supervised and unsupervised models Architect and implement deep learning solutions (CNNs, Transformers) with PyTorch Develop and fine-tune Large Language Models (LLMs) and build LLM-driven applications Implement Retrieval-Augmented Generation (RAG) pipelines and integrate with vector databases Build robust pipelines to deploy models at scale (Docker, Kubernetes, CI/CD) Ingest, clean and transform large datasets using libraries like pandas, NumPy, and Spark Automate training and serving workflows with Airflow or similar orchestration tools Monitor model performance in production; iterate on drift detection and retraining strategies Implement LLMOps practices for automated testing, evaluation, and monitoring of LLMs Write production-grade Python code following SOLID principles, unit tests and code reviews Collaborate in Agile (Scrum) ceremonies; track work in JIRA Document architecture and workflows using PlantUML or comparable tools Communicate analysis, design and results clearly in English Partner with DevOps, data engineering and product teams to align on requirements and SLAs