Job Location: Bangalore/Bengaluru
Role Definition
- Develop and deliver a MLOps (CI/CD) pipeline in AWS cloud and Sagemaker pipeline for ML projects.
- Automate and streamline ML operations and processes.
- Interacting with Data engineering team and ML engineering team to streamline the ML ops process.
- Produce build and deployment automation scripts to integrate between services.
- Manage the infrastructure and pipelines needed to bring latest models and code into the production.
- Software deployment and configuration management in both DEV, testing and Production environments.
- Execute best practices in version control and continuous integration / delivery.
- Build and maintain tools for deployment, monitoring, and operations. Also troubleshoot and resolve issues in development, testing, and production environments
- Day-to-day monitoring of the Production service delivery environment to ensure all services and applications are operating optimally and SLAs are met.
- Keep up with emerging best practices in ML-Ops and drive adoption as necessary.
- Work collaboratively with Data Scientists and Data engineers to deploy and operate systems
Value you will deliver
- Achieve top line and bottom line targets for the account/portfolio
- Detailed account plan and achieving on the same on a quarter on quarter basis
- Formal engagement plan for liaising with key senior customer stakeholders, and facilitating Altimetrik leadership connects with client executives via a multi-tier approach
- Lead and influence an engaged and effective workforce and that is fully integrated with Altimetrik vision, values and purpose
Our ideal candidate comes with
- Overall should have min of 7+ years of exp
- Minimum 3 years experience in industrialization of Data Science projects (MLOps) on AWS is a must .
- Need to be proficient in Sagemaker libraries/SDK for ML-OPS industrialization in addition to proficiency in CodePipelines, Lambda, Step Functions, EventBridge etc.
- Should have a thorough understanding and appreciation of AI/ML lifecycle its challenges. Hence an appreciation of automated MLOps pipelines to support experimentation, continuous integration, continuous delivery and continuous training.
- Experience in creating complex workflows with features like
- Auto scaling and parallelism.
- Automated startup/shutdown resources, when idle.
- Must include a housekeeping part, to clean up any runtime residues.
- Build and implement software tool-chain, processes, and templates for Data Science and ML team for development and deployment environments.
- Experience in logging and monitoring ML models in production.
- Experience in tracking ML lineage with Sagemaker model registry.
- Creating an audit trail of model components such as training data, platform configurations, model parameters.
- Experience in creating Docker images for training and inference models.
- Experience with scripting and coding using Python, Shell.
- Expertise in below technologies:
- AWS – EC2
- AWS Sagemaker pipeline
- AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, AWS CodeDeploy
- StepFunctions
- Docker
- CloudFormation
- Sagemaker model registry
- Docker
- ECR
- Sagemaker model monitor
- Cloudwatch and cloud trail
- SNS Notification
- Airflow, Jenkins and Gitlab
Technical/Functional competency
- IT software development, digital solutions, digital transformation background is mandatory.
- Good understanding of technology, software development methodologies especially as relating to developing custom software for customers using agile principles
Behavioral competency
- Strong problem solving skills
- Ability to lead initiatives and people toward common goals
- Working knowledge of systems infrastructure
- Excellent oral and written communication, presentation, and analytical skills
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