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SWITS DIGITAL Private Limited
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Data Science Engineer

Data Science Engineer

SWITS DIGITAL Private Limited

4 - 8 years

0 Lacs

bengaluru karnataka india

Posted:2 days ago| Platform:

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Skills Required

data scaling learning ml aws engineering design cortex airflow mlflow reliability model monitoring versioning sagemaker latency compliance governance sql orchestration scheduling automation tracking management training inference python programming numpy etl storage docker kubernetes terraform tensorflow pytorch jenkins github gitlab communication collaborative

Work Mode

On-site

Job Type

Full Time

Job Description

Position:

Senior Data Science Engineer

Experience:

4 - 8 Years

Location: Bengaluru, Pune, Mumbai

About The Role

We are seeking a highly skilled

Senior Data Science Engineer

with strong experience in building, deploying, and scaling machine learning solutions. The ideal candidate will have hands-on expertise in

Snowflake Cortex

Airflow

ML Ops

MLflow

, and

AWS SageMaker

, with a solid understanding of data engineering workflows and end-to-end ML lifecycle management.

Key Responsibilities

Design, build, and deploy scalable machine learning pipelines using Snowflake Cortex, Airflow, and MLflow.
Develop and optimize ML models, ensuring high performance and reliability in production environments.
Implement ML Ops best practices including CI/CD, model monitoring, versioning, and automated retraining.
Build end-to-end machine learning solutions on AWS SageMaker, integrating with data lakes and data warehouses.
Collaborate with data scientists, data engineers, and product teams to deliver ML features and insights.
Optimize data pipelines and model execution using Snowflake and cloud resources.
Monitor ML systems, troubleshoot production issues, and improve model accuracy/latency.
Document workflows, maintain model repositories, and ensure compliance with governance standards.

Required Skills (Mandatory)

Strong experience with Snowflake Cortex and Snowflake SQL.
Expertise in Airflow (DAG creation, orchestration, scheduling).
Hands-on ML Ops experience (CI/CD, model monitoring, automation).
Proficiency in MLflow (tracking, model registry, experiment management).
Solid knowledge of AWS SageMaker (training jobs, inference endpoints, pipelines).
Strong Python programming skills (Pandas, NumPy, Scikit-learn).
Experience with cloud environments (AWS preferred).
Data engineering fundamentals – ETL pipelines, data modelling, storage formats.

Good to Have

Experience with Docker, Kubernetes, Terraform.
Knowledge of deep learning frameworks (TensorFlow, PyTorch).
Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI).
Understanding of large-scale distributed systems.

Soft Skills

Strong problem-solving and analytical thinking.
Excellent communication and stakeholder management.
Ability to work in a fast-paced, collaborative environment.

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