MLOps PLatform Engineer /architect

Hi 

hope you are doing good.!

Position : MLOps platform Engineer/Architect

Location: Remote

Platform Architecture

· Assess the existing Domino, Kubernetes, GitLab, Chalk, and Cloudera environments.

· Define the target architecture for authoring, versioning, validating, deploying, and operating feature definitions.

· Define clear system boundaries and responsibilities among Domino, GitLab, Chalk, Kubernetes, object storage, and upstream data platforms.

· Document key architecture decisions, dependencies, risks, and integration contracts.

· Ensure that the architecture supports maintainability, traceability, security, and operational ownership.

Feature Development Lifecycle

· Establish the workflow for developing Chalk compatible Python feature definitions in Domino.

· Define how feature code and associated configuration will be version controlled in GitLab.

· Design validation and approval controls for changes to feature definitions.

· Establish traceability from source code changes through deployment and production operation.

· Define development and production environment separation, promotion procedures, rollback, and recovery mechanisms.

CI CD and Platform Integration

· Design and implement the deployment pipeline from GitLab into the Chalk environment.

· Determine the supported integration method for publishing code and configuration into Chalk managed storage.

· Integrate the deployment process with the on premises Kubernetes environment.

· Automate environment specific configuration and deployment validation.

· Minimize manual engineering handoffs and ensure deployment processes are repeatable, auditable, and recoverable.

Required Qualifications

· Demonstrated senior level experience designing and implementing production MLOps or machine learning platform architectures.

· Strong hands on experience with Kubernetes based platforms in on premises or private cloud environments.

· Strong Python engineering experience.

· Experience designing CI CD pipelines for Python applications, platform configuration, or machine learning artifacts.

· Experience with Git based development and deployment workflows, preferably GitLab.

· Experience integrating applications with object storage, including S3 compatible interfaces.

· Experience designing secure integrations across compute platforms, source control, storage, and enterprise data systems.

· Understanding of feature engineering, feature lifecycle management, online and batch feature processing, and production feature delivery.

· Experience defining development to production promotion, validation, rollback, and audit controls.

· Ability to troubleshoot distributed systems across application, container, storage, network, and data layers.

· Ability to produce clear architecture documentation, implementation specifications, and operational runbooks.

· Ability to work directly with both data scientists and infrastructure engineers.