Machine Learning (ML)
Deep LearningPrimary 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
Shubham Vishwakarma
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Full Stack Developer - Averlon