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

ML Engineer II - Aws, Aws Cloud

at Global Digital Transformation Solutions Provider

Agency job
6 - 12 yrs
₹25L - ₹30L / yr
Pune
Skills
skill iconMachine Learning (ML)
AWS CloudFormation
Online machine learning
skill iconAmazon Web Services (AWS)
ECS
skill iconKubernetes
Amazon Redshift
Glue semantics
Amazon S3
Amazon EC2
AWS Lambda
skill iconDeep Learning
Large Language Models (LLM)
Large Language Models (LLM) tuning
CI/CD
cicd
Data cleansing
Prompt engineering
SQL
Data manipulation
Apache Kafka
Neo4J
MLOps
Performance Evaluation
skill iconDocker

MUST-HAVES: 

  • Machine Learning + Aws + (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sage maker
  • Notice period - 0 to 15 days only 
  • Hybrid work mode- 3 days office, 2 days at home


SKILLS: AWS, AWS CLOUD, AMAZON REDSHIFT, EKS


ADDITIONAL GUIDELINES:

  • Interview process: - 2 Technical round + 1 Client round
  • 3 days in office, Hybrid model. 


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, Chaos search logs, etc. for troubleshooting; Other tech touch points are Scylla DB (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 Sage maker 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)
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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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