Skills

Full description

Responsibilities

Designing, training, and evaluating machine learning models (supervised, unsupervised, NLP, etc.).
Building scalable data and ML pipelines using modern tools.
Collaborating with subject matter experts and analysts to prepare training datasets.
Deploying models for production (batch or real-time inference).
Monitoring and maintaining model performance and data quality.
Optimizing models for performance, interpretability, and cost.
Documenting ML workflows and ensuring reproducibility.

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Must have requirements

Experience as a Machine Learning Engineer or in a similar role for 2+ years.
Proficiency in Python, including hands-on experience with libraries such as scikit-learn, pandas, NumPy, and matplotlib.
Strong understanding of core ML concepts – regression, classification, clustering, model validation, and performance metrics.
Practical experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Proven experience building, training, and deploying ML models using AWS SageMaker.
Familiarity with AWS Bedrock for working with foundation and generative models (e.g., fine-tuning and orchestration of LLMs).
Hands-on experience with data preprocessing, feature engineering, and model evaluation.
Knowledge of SQL and experience working with structured and semi-structured datasets.
Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask; model packaging and containerization with Docker).
Exposure to MLOps practices – pipeline automation, model versioning, monitoring, and reproducibility.
Familiarity with version control systems (e.g., Git).
Strong analytical thinking, communication, and problem-solving skills.
Willingness to stay current with emerging ML techniques, frameworks, and cloud AI tools.
Level of English – from Upper-Intermediate and above.
Level of German – from Intermediate+ and above.

Location: Remote - Anywhere

Machine Learning Engineer

Compensation

$60K–$80K / yr

Location

Remote, Worldwide

Work setup

Remote

Seniority

Not specified

Employment

Not specified

Experience

2+ years

Responsibilities

Designing, training, and evaluating machine learning models (supervised, unsupervised, NLP, etc.).
Building scalable data and ML pipelines using modern tools.
Collaborating with subject matter experts and analysts to prepare training datasets.
Deploying models for production (batch or real-time inference).
Monitoring and maintaining model performance and data quality.
Optimizing models for performance, interpretability, and cost.
Documenting ML workflows and ensuring reproducibility.

Show less
Must have requirements

Experience as a Machine Learning Engineer or in a similar role for 2+ years.
Proficiency in Python, including hands-on experience with libraries such as scikit-learn, pandas, NumPy, and matplotlib.
Strong understanding of core ML concepts – regression, classification, clustering, model validation, and performance metrics.
Practical experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Proven experience building, training, and deploying ML models using AWS SageMaker.
Familiarity with AWS Bedrock for working with foundation and generative models (e.g., fine-tuning and orchestration of LLMs).
Hands-on experience with data preprocessing, feature engineering, and model evaluation.
Knowledge of SQL and experience working with structured and semi-structured datasets.
Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask; model packaging and containerization with Docker).
Exposure to MLOps practices – pipeline automation, model versioning, monitoring, and reproducibility.
Familiarity with version control systems (e.g., Git).
Strong analytical thinking, communication, and problem-solving skills.
Willingness to stay current with emerging ML techniques, frameworks, and cloud AI tools.
Level of English – from Upper-Intermediate and above.
Level of German – from Intermediate+ and above.

Location: Remote - Anywhere