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4 Trustana Jobs

Principal Data Engineer For ML

Trustana

5-9 years

Gurgaon / Gurugram

1 vacancy

Trustana

posted 3mon ago

Job Role Insights

Key skills for the job

Data Science Computer Science Python SQL Automation Testing Machine Learning

+ 4 more

Job Description

As a **Machine Learning Data Engineer**, you will be responsible for designing, building, and maintaining scalable data pipelines and ML infrastructure. Collaborating with cross-functional teams, you will ensure the data and ML pipelines support machine learning model development, deployment, and monitoring needs. You will focus on enabling machine learning use cases like data preparation or model serving, while incorporating MLOps best practices to enhance automation, scalability, and model lifecycle management.

Key Responsibilities :

Design, develop, and maintain scalable data and ML pipelines to support machine learning use cases.
Collaborate with Data Science, Engineering, and Product teams to define data and ML requirements and technical specifications.
Implement ETL/ELT processes to prepare data for machine learning models, ensuring data quality and integrity.
Design and deploy automated model training, validation, and monitoring workflows using MLOps best practices.
Develop reusable components for data and feature engineering, model training, and model serving.
Ensure team visibility through metric measurements and insights, focusing on model performance and health.
Establish best practices for ML model versioning, deployment, and monitoring to support continuous integration and deployment (CI/CD).

Qualifications :

Bachelors degree in Data Science, Computer Science, Engineering, or a related technical field.
5+ years of experience in building data and ML pipelines for machine learning projects.
Expertise in data modeling, architecture, and SQL.
Proficiency in programming languages (Python, Scala, Java) and experience with ML libraries/frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
Experience with Big Data frameworks like Hadoop and Spark.
Proven experience with cloud-based ML and data solutions (e.g., AWS Sagemaker, GCP AI Platform, Azure ML).
Strong understanding of MLOps concepts, including model lifecycle management, CI/CD, and model monitoring.
Familiarity with containerization tools (e.g., Docker, Kubernetes) to facilitate scalable ML deployments.

What We Offer:

Competitive salary plus performance-based bonuses.
Opportunity to work in an innovative environment at the forefront of data solutions.
Professional development and career growth opportunities. We are scaling!
Comprehensive benefits package.

Employment Type: Full Time, Permanent

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