Senior Associate Manager

2 months ago

Bangalore, Karnataka, India Promaynaov Advisory Services Pvt Ltd Full time

Please find below job details :

Role : Senior Associate Manager - MLOps Engineer

Experience : 5 - 8 years

Location : Bangalore

Mode : WFO (5 days)

Employment mode : Contractual

Contract Period : 1 year

Job Description :

The MLOps engineer's role is service focused and will create data pipeline and engineering infrastructure to support our enterprise machine learning systems. This role will collaborate with data scientists and statisticians from various functions to facilitate and lead scientific and/or business knowledge discovery, insights, and forecasting. The MLOps engineer will be part of a highly collaborative and cross-functional team of technology and data experts working on solving complex scientific and business challenges using cutting edge data and analytics technologies. The MLOps Engineer will be responsible for designing, implementing, and maintaining machine learning infrastructure, pipelines, and workflows. This role will require a deep understanding of data management, software development, and cloud computing. The successful candidate will work closely with data scientists, software engineers, and other stakeholders to ensure that machine learning models are deployed, monitored, and updated efficiently and effectively.

The MLOps engineer is expected to work with teams spread globally across different time zones.

Role & Responsibilities :

- Deploy and maintain machine learning models, pipelines, and workflows in production environment.

- Re-package (deployment process) ML models that have been developed in the non-production ML environment by ML Teams for deployment to the production ML environment.

- Perform the required MLOps engineering development to refactor the non-production ML model implementation to an "ML as Code" implementation.

- Create, manage, and execute ServiceNow change requests in accordance with the IT Change Management process to manage the deployment of new models.

- Build and maintain machine learning infrastructure that is scalable, reliable, and efficient.

- Collaborate with data scientists and software engineers to design and implement machine learning workflows.

- Implement monitoring and logging tools to ensure that machine learning models are performing optimally.

- Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems.

- Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.

- Support model development, with an emphasis on auditability, versioning, and data security.

- Create and maintain technical documentation for machine learning infrastructure and workflows.

- Stay up to date with the latest developments in machine learning and cloud computing technologies.

- Provide expert data PaaS on Azure storage; big data platform services; server-less architectures; Azure SQL DB; NoSQL databases and secure, automated data pipelines.

- Work collaboratively and use sound judgment in developing robust solutions while seeking guidance on complex problems.

Basic Qualifications (Must have) :

- Bachelor's or master's degree in computer science, engineering or related field.

- 5+ years of experience in software development, machine learning engineering or related field.

- Strong understanding of machine learning concepts and frameworks.

- Hand-on experience in Python.

- Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git.

- Experience in developing and deploying machine learning models in a production environment.

- Experience working with cloud computing and database systems.

- Experience building custom integrations between cloud-based systems using APIs.

- Experience developing and maintaining ML systems built with open-source tools.

- Experience developing with containers and Kubernetes in cloud computing environments.

- Ability to translate business needs to technical requirements.

- At least 2 years of data pipeline and data product design, development, delivery experience and deploying ETL/ELT solutions on Azure Data Factory.

- Strong analytical and problem-solving skills.

Good to Have Skills :

- Cloud migration methodologies and processes including tools like Azure Data Factory, Event Hub, etc.

- Experience in using Hadoop File Formats and compression techniques.

- DevOps on an Azure platform.

- Experience working with Developer tools such as Visual Studio, GitLab's, Jenkins, etc.

- Experience with private and public cloud architectures, pros/cons, and migration considerations.

- Proven ability to work independently.

- Proven ability to work in a team-oriented environment and work collaboratively in a problem-solving environment.

- Experience with MLOps in Azure preferred.

- Azure native data/big-data tools, technologies and services experience including - Storage BLOBS, ADLS, Azure SQL DB, and SQL Data Warehouse.

- Excellent written and oral communication and interpersonal skills.

- Excellent organizational and multi-tasking.

(ref:hirist.tech)

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