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As a ML / NLP Engineer you will play a key role in driving the development, deployment and optimization of advanced machine learning and natural language processing solutions that directly support the organization’s strategic goals. You would be responsible for designing end-to-end ML pipelines, ensuring production readiness and delivering scalable models that generate measurable business impact. Acting as a technical expert, you will provide leadership in adopting MLOps best practices, guiding junior engineers and fostering innovation within the team.
Job Responsibility:
Develop, optimize, and evaluate new Machine Learning (ML) and Statistical models
Design, implement, and deploy scalable ML and Natural Language Processing (NLP) models tailored to specific business needs
Select suitable algorithms and fine-tune models to achieve optimal performance
Ensure models are production-ready and integrate them seamlessly into existing systems
Oversee the entire ML pipeline, from data collection and preprocessing to model training, evaluation, and deployment
Implement monitoring and maintenance strategies to ensure the model's performance remains consistent over time
Develop data pipelines and preprocess data for training and inference to gain valuable insights into complex datasets, enabling informed decision-making
Work closely with data scientists, software engineers, product managers, and other stakeholders to align ML initiatives with business goals
Implement evaluation metrics and conduct cross-validation to assess model effectiveness and reliability
Present project progress and outcomes to executive leadership
Requirements:
Master’s degree in Data Science, ML or a related field
Over 3 years of experience in ML/NLP Engineering
Additional MLOps and AI/Data Science experience is desired
Advanced experience with Python for complex applications and ML models
In-depth understanding of Artificial Intelligence principle
Excellent verbal and written communication skills in English
Hands-on experience with ML frameworks such as PyTorch, TensorFlow and Scikit-learn, as well as Hugging Face ecosystem
Strong expertise in NLP techniques, including Tokenization and Named Entity Recognition (NER)
Proficiency using Pandas and Polars, along with experience in building interactive data applications and prototypes using Streamlit
Solid experience with tools such as FastAPI, Celery and Keycloak, as well as working with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines