ML Engineer
ML Engineer
Posted on :
03-05-2025
Employer Active
1 Vacancy
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Job Description
Requirements:
- Strong handson experience with classification models and machine learning libraries such as XGBoost scikitlearn and other Pythonbased ML tools.
- Proficient in Python programming with solid experience in data handling libraries like Pandas.
- Experienced in working with text data including preprocessing tokenization feature extraction (bagofwords TFIDF embeddings) and integrating text features into traditional ML models.
- Familiar with NLP libraries such as spaCy NLTK HuggingFace Transformers and dimensionality reduction techniques like PCA SVD and UMAP for text.
- Handson experience with neural networks and a strong understanding of ML fundamentals including feature engineering model evaluation hyperparameter tuning biasvariance tradeoff and interpretability.
- Experienced in MLOps practices such as model versioning deployment monitoring lifecycle management and retraining pipelines using tools like MLflow or Weights & Biases.
- Skilled in AWS services relevant to ML workflows especially Lambda S3 and SageMaker.
- Proficient in reading understanding and improving productiongrade Python codebases with familiarity in version control (Git) code reviews and agile development practices.
- Comfortable with structured data systems and handling large datasets using SQL BigQuery or similar tools.
- Strong communication skills with the ability to present technical concepts to both technical and nontechnical stakeholders.
- A proactive problemsolver and critical thinker who seeks continuous improvement identifies gaps and leads new initiatives.
- A collaborative team player who combines strong analytical thinking with a handson approach to both optimizing existing models and building innovative solutions.
Responsibilities:
- Deeply understand the current ML models architecture feature engineering pipeline and training methodology with a strong focus on textbased data inputs.
- Perform critical evaluations of existing feature engineering and model decisions.
- Analyze model performance identify opportunities for optimization and address technical debt.
- Propose and implement improvements to feature processing pipelines including better handling of text features (e.g. TFIDF embeddings and dimensionality reduction).
- Lead the design and development of new machine learning models or approaches as the product evolves.
- Maintain and improve model training validation and deployment pipelines for production.
- Collaborate closely with data scientists software engineers and product teams to align modeling strategies with business objectives.
- Ensure robust documentation of model internals evaluation findings and improvement strategies.
Employment Type
Full Time
Key Skills
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