MLOps Engineer

at Reputed client of People First

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Chennai, Bengaluru (Bangalore), Gurugram
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2 - 9 yrs
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₹10L - ₹30L / yr
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Full time
Skills
Docker
Kubernetes
DevOps
Jenkins
CI/CD
Google Cloud Storage
Microsoft Windows Azure
Amazon Web Services (AWS)
Mlops
Job Description:
As a MLOps Engineer in QuantumBlack you will:

Develop and deploy technology that enables data scientists and data engineers to build, productionize and deploy machine learning models following best practices. Work to set the standards for SWE and
DevOps practices within multi-disciplinary delivery teams

Choose and use the right cloud services, DevOps tooling and ML tooling for the team to be able to produce high-quality code that allows your team to release to production
.
Build modern, scalable, and secure CI/CD pipelines to automate development and deployment
workflows used by data scientists (ML pipelines) and data engineers (Data pipelines)

Shape and support next generation technology that enables scaling ML products and platforms. Bring
expertise in cloud to enable ML use case development, including MLOps

Our Tech Stack-

We leverage AWS, Google Cloud, Azure, Databricks, Docker, Kubernetes, Argo, Airflow, Kedro, Python,
Terraform, GitHub actions, MLFlow, Node.JS, React, Typescript amongst others in our projects

Key Skills:

• Excellent hands-on expert knowledge of cloud platform infrastructure and administration
(Azure/AWS/GCP) with strong knowledge of cloud services integration, and cloud security

• Expertise setting up CI/CD processes, building and maintaining secure DevOps pipelines with at
least 2 major DevOps stacks (e.g., Azure DevOps, Gitlab, Argo)

• Experience with modern development methods and tooling: Containers (e.g., docker) and
container orchestration (K8s), CI/CD tools (e.g., Circle CI, Jenkins, GitHub actions, Azure
DevOps), version control (Git, GitHub, GitLab), orchestration/DAGs tools (e.g., Argo, Airflow,
Kubeflow)

• Hands-on coding skills Python 3 (e.g., API including automated testing frameworks and libraries
(e.g., pytest) and Infrastructure as Code (e.g., Terraform) and Kubernetes artifacts (e.g.,
deployments, operators, helm charts)

• Experience setting up at least one contemporary MLOps tooling (e.g., experiment tracking,
model governance, packaging, deployment, feature store)

• Practical knowledge delivering and maintaining production software such as APIs and cloud
infrastructure

• Knowledge of SQL (intermediate level or more preferred) and familiarity working with at least
one common RDBMS (MySQL, Postgres, SQL Server, Oracle)

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