ML Engineer

MNC

ML Engineer

Fragma Data Systems

Company

Home

ML Engineer at MNC · Bengaluru (Bangalore) · 3 - 7 years · ₹15L - ₹20L / yr · Posted 11 Aug 2021

ML Engineer

at MNC

Agency job

via Fragma Data Systems

3 - 7 yrs

₹15L - ₹20L / yr

Bengaluru (Bangalore)

Skills

Machine Learning (ML)

Deep Learning

Load Testing

Performance Testing

Stress Testing

Test Planning

Primary Responsibilities

Understand current state architecture, including pain points.
Create and document future state architectural options to address specific issues or initiatives using Machine Learning.
Innovate and scale architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization.
Develop CI/CD & ML pipelines that help to achieve end-to-end ML model development lifecycle from data preparation and feature engineering to model deployment and retraining.
Provide recommendations around security, cost, performance, reliability, and operational efficiency and implement them
Provide thought leadership around the use of industry standard tools and models (including commercially available models and tools) by leveraging experience and current industry trends.
Collaborate with the Enterprise Architect, consulting partners and client IT team as warranted to establish and implement strategic initiatives.
Make recommendations and assess proposals for optimization.
Identify operational issues and recommend and implement strategies to resolve problems.

Must have:

3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development
Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others
Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment.
Strong programming skillset with high proficiency in Python, R, etc.
Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc.
Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop
Knowledge of pure and applied math, ML and DL frameworks, and ML techniques, such as random forest and neural networks
Ability to collaborate with Data scientist, Data Engineers, Leaders, and other IT teams
Ability to work with multiple projects and work streams at one time. Must be able to deliver results based upon project deadlines.
Willing to flex daily work schedule to allow for time-zone differences for global team communications
Strong interpersonal and communication skills

Primary Responsibilities

Understand current state architecture, including pain points.
Create and document future state architectural options to address specific issues or initiatives using Machine Learning.
Innovate and scale architectural best practices around building and operating ML workloads by collaborating with stakeholders across the organization.
Develop CI/CD & ML pipelines that help to achieve end-to-end ML model development lifecycle from data preparation and feature engineering to model deployment and retraining.
Provide recommendations around security, cost, performance, reliability, and operational efficiency and implement them
Provide thought leadership around the use of industry standard tools and models (including commercially available models and tools) by leveraging experience and current industry trends.
Collaborate with the Enterprise Architect, consulting partners and client IT team as warranted to establish and implement strategic initiatives.
Make recommendations and assess proposals for optimization.
Identify operational issues and recommend and implement strategies to resolve problems.

Must have:

3+ years of experience in developing CI/CD & ML pipelines for end-to-end ML model/workloads development
Strong knowledge in ML operations and DevOps workflows and tools such as Git, AWS CodeBuild & CodePipeline, Jenkins, AWS CloudFormation, and others
Background in ML algorithm development, AI/ML Platforms, Deep Learning, ML Operations in the cloud environment.
Strong programming skillset with high proficiency in Python, R, etc.
Strong knowledge of AWS cloud and its technologies such as S3, Redshift, Athena, Glue, SageMaker etc.
Working knowledge of databases, data warehouses, data preparation and integration tools, along with big data parallel processing layers such as Apache Spark or Hadoop
Knowledge of pure and applied math, ML and DL frameworks, and ML techniques, such as random forest and neural networks
Ability to collaborate with Data scientist, Data Engineers, Leaders, and other IT teams
Ability to work with multiple projects and work streams at one time. Must be able to deliver results based upon project deadlines.
Willing to flex daily work schedule to allow for time-zone differences for global team communications
Strong interpersonal and communication skills

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