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.