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Lead I - ML Engineering

0 years

0 Lacs

pune maharashtra india

Posted:1 day ago| Platform: Linkedin logo

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Skills Required

ml engineering design test learning model efficiency development algorithms data training integration deployment collaboration software programming testing sql kafka monitoring sagemaker pipeline analyze aws redshift containerization orchestration docker kubernetes

Work Mode

On-site

Job Type

Full Time

Job Description

Role Description

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

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