Lead Machine Learning Data Engineer [T500-17894]

ANSR; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Join a leading company as a Machine Learning Engineer, where you'll design and maintain scalable data pipelines and deploy machine learning models. Collaborate with global teams to optimize model performance and ensure compliance with best practices. This role requires strong problem-solving skills and experience with various ML frameworks and CI/CD tools.

Qualifications

• 8+ years of experience in MLOps, machine learning, or data engineering.
• Strong experience with CI/CD tools to automate deployment processes.

Responsibilities

• Design, build, and maintain scalable data pipelines for ML models.
• Develop and implement automated workflows for model integration.

Skills

Problem Solving

Communication

Analytical Skills

Education

Bachelor’s degree in computer science

Master’s degree

Tools

TensorFlow

PyTorch

Scikit-learn

Jenkins

GitLab CI

CircleCI

Python

Java

Scala

SQL

NoSQL

Docker

Kubernetes

Apache Airflow

Apache Kafka

Snowflake

Azure ML

AWS Sagemaker

MLFLow

Job description

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As our rockstar ML and Data Science Engineer, you'll be the maestro orchestrating the seamless flow of brilliant machine learning models from the lab into the real world! You'll be the architect, the builder, and the guardian of the pipelines that make magic happen. Get ready to team up with the brainy data scientists, the wizardly data engineers, and the cool-headed IT ops crew to make model deployment lightning fast, keep a hawk-eye on their performance, and fine-tune them until they sing. It’s a global adventure and would entail collaboration with global teams across time zones to ensure operational continuity.

THE GIG

• Development: Design, build, and maintain scalable data pipelines to support machine learning models
• Model Deployment: Develop and implement automated workflows for seamless production integration of machine learning models using CI/CD practices.
• Infrastructure Management: Design and maintain scalable infrastructure for ML workloads, utilizing cloud services (AWS, GCP, Azure) and container orchestration (e.g., Kubernetes).
• Monitoring & Performance Optimization: Establish monitoring frameworks to assess model performance, retraining needs, and data drift, ensuring models deliver optimal business value.
• Data Management: Ensure proper data pipeline processes, including data cleaning, transformation, and management, is in place to support model training and inference.
• Security & Compliance: Implement best practices related to model security, data privacy, and compliance with applicable regulations.
• Documentation and best practices: Produces architecture and design artifacts for complex applications, accountable for design constraints and best practices for code development.

THE STUFF THAT SETS YOU APART

Required Experience and Skills – Our Must Haves

• Bachelor’s degree in computer science, Engineering, Data Science, or a related field. Master’s degree preferred.
• 8+ years of experience in MLOps, machine learning, or data engineering.
• Proficiency with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and understanding of supervised and unsupervised learning algorithms.
• Strong experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) to automate deployment processes.
• Adept in programming languages such as Python, Java, or Scala, with solid knowledge of SQL and NoSQL databases.
• Familiarity with cloud services (AWS, Azure, Google Cloud) and containerization technologies (Docker, Kubernetes).
• Experience with data pipeline frameworks (e.g., Apache Airflow, Apache Kafka).
• Excellent problem-solving and analytical skills, with the ability to troubleshoot complex issues.
• Strong communication skills to work with cross-functional teams and present findings effectively to non-technical stakeholders.
• Comfortable with Snowflake centric MLOps leveraging Snowpark developer framework, Snowflake UDFs/store procedures and other SF capabilities for feature engineering and version control.
• Having experience on any of these MLOps platform Azure ML/ AWS Sagemaker/ MLFLow(Databricks)

Nice to have:

• Azure ML integration with Snowflake: Use Snowflake as the data layer, Azure ML for model training, and push back results for inference.
• Anomaly Detection & AutoML: Building modular components that can run and scale via Snowpark.
• Model Monitoring: Statistical drift detection using Snowflake + Power BI dashboards.
• Data Build tool (DBT): Modular, testable, and documented transformations feeding ML workflows.

Core Competencies

• Strong problem-solving skills with a proactive approach to optimization.
• Excellent communication skills and ability to collaborate across global teams and time zones.
• Experience in designing and implementing MLOps best practices and frameworks.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Information Technology and Engineering

• Industries

  Retail Apparel and Fashion

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