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Enterprise Minds
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Data Science Engineer

Data Science Engineer

Enterprise Minds

6 - 9 years

25 - 32 Lacs

hyderabad

Posted:1 day ago| Platform:

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Skills Required

algorithms python sql data structures software engineering continuous integration kubernetes scikit-learn ci/cd microsoft azure power bi machine learning azure devops docker tensorflow tableau data science gcp devops pytorch agile aws etl ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

?? Location: Hyderabad
????? Experience: 2–4 Years
?? Employment Type: Full-time

About the Role:
We are seeking a Data Science Engineer with 2 to 6 years of experience who is passionate about building scalable, robust, and high-performance machine learning systems. You will play a key role in bridging the gap between data science research and production, ensuring smooth deployment, monitoring, and maintenance of ML models and data pipelines in cloud environments.

Key Responsibilities:

Collaborate with data scientists and analysts to operationalize ML models and deploy them into scalable production systems.

Design, build, and maintain automated pipelines for data ingestion, transformation, model training, validation, deployment, and monitoring.

Implement MLOps best practices for model tracking, versioning, testing, and automated retraining using tools like MLflow, Kubeflow, or SageMaker .

Work with large-scale data platforms and ensure data quality, integrity, and accessibility for training and inference.

Optimize system performance and model efficiency using containerization tools such as Docker and Kubernetes .

Collaborate in an Agile/Scrum environment and actively contribute to sprint planning, reviews, and retrospectives.

Monitor and troubleshoot models in production for accuracy, drift, and performance degradation.

Required
Skills:

2 to 4 years of hands-on experience in data science engineering or ML engineering roles.

Proficiency in Python with solid understanding of data structures, algorithms , and software engineering practices .

Practical experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch .

Experience with cloud platforms (AWS, Azure, or GCP) and their ML/AI services (SageMaker, Azure ML, etc.).

Sound knowledge of MLOps concepts and tools like MLflow, Airflow, Kubeflow , or similar.

Strong SQL skills and familiarity with data wrangling, cleaning, and transformation .

Preferred (Secondary)
Skills:

Experience with Docker and Kubernetes for scalable model deployment.

Knowledge of data engineering pipelines , batch/stream processing, and ETL tools.

Familiarity with data visualization tools like Tableau, Power BI, or Plotly for reporting and dashboards.

Understanding of CI/CD pipelines and DevOps practices .

Exposure to Agile methodologies and working in cross-functional teams.

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Data Science Engineer