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Talentgigs Linkedin · Posted yesterday

Machine Learning Engineer

India

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Indexed description

Job Role: ML Engineer

Years of Experience: 3 - 6 Yrs

Work Mode: Work From Office (Coimbatore)


JD:

Roles, Responsibilities and Duties / Tasks and duties that define the role

· Develop and implement machine learning algorithms and models for various applications. · Conduct exploratory data analysis to uncover patterns and insights. · Design and develop machine learning pipelines for data ingestion, pre-processing, feature engineering, model training, and evaluation. · Optimize machine learning models for performance and scalability. · Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. · Stay up-to-date with the latest advancements in machine learning and deep learning. · Experiment with new ML techniques and technologies.


Must-Have Skills

· Strong programming skills in Python and proficiency in ML frameworks (TensorFlow, PyTorch, Keras). · Experience with data pre-processing, feature engineering, and model selection. · Knowledge of various machine learning algorithms (regression, classification, clustering, etc.) and their applications. · Proficiency in statistical analysis and data visualization. · Excellent problem-solving and analytical skills. · Strong communication and collaboration skills. · Experience with deep learning architectures (CNN, RNN, LSTM, etc.). · Knowledge of ML Ops and model deployment practices. · Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. · 3 to 6 years of hands-on experience in a machine learning engineering role


Good to have Skills

· Experience with cloud platforms (AWS, GCP, Azure). · Familiarity with MLOps tooling (MLflow, Kubeflow, Airflow, Docker, Kubernetes). · Exposure to NLP or computer vision projects. · Experience building CI/CD pipelines for ML model deployment. · Familiarity with SQL and NoSQL databases. · Prior experience working with US-based clients or in a client-delivery environment

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