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ML Engineer II - Aws, Aws Cloud
Global Digital Transformation Solutions Provider
ML Engineer II - Aws, Aws Cloud
Global Digital Transformation Solutions Provider's logo

ML Engineer II - Aws, Aws Cloud

at Global Digital Transformation Solutions Provider

Agency job
6 - 12 yrs
₹15L - ₹30L / yr
Pune
Skills
skill iconMachine Learning (ML)
skill iconAmazon Web Services (AWS)
skill iconKubernetes
ECS
Amazon Redshift
Glue semantics
SAGE
Statistical Modeling
Algorithms
Large Language Models (LLM)
CI/CD
Data engineering
SQL
Data manipulation
Neo4J
MLOps
AWS Lambda
skill iconDocker
AWS CloudFormation

Core Responsibilities:

  • The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
  • Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
  • Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
  • Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
  • System Integration: Integrate models into existing systems and workflows.
  • Model Deployment: Deploy models to production environments and monitor performance.
  • Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
  • Continuous Improvement: Identify areas for improvement in model performance and systems.

 

Skills:

  • Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
  • Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph
  • Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
  • Knowledge of model monitoring and performance evaluation.

 

Required experience:

  • Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements
  • AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
  • AWS data: Redshift, Glue
  • Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

 

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

 

Must-Haves

Machine Learning +Aws+ (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sagemaker

Notice period - 0 to 15days only

Hybrid work mode- 3 days office, 2 days at home

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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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