MLOps Engineer

Veridic Solutions LLC; Austin (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Veridic Solutions LLC seeks a Senior MLOps Engineer to design and deliver a next‑generation ML Platform‑as‑a‑Product. You will abstract infrastructure complexities so ML engineers and data scientists can write, train, deploy, and monitor models in production.

Responsibilities cover standardizing development environments, building scalable training pipelines with Vertex AI, Kubeflow, and Databricks, and implementing end‑to‑end observability with automated retrain loops on GKE/Vertex Endpoints.

Qualifications

• Experience building a unified ML Platform-as-a-Service for internal engineering use.
• Strong knowledge of MLOps, CI/CD for ML, automated testing, and canary deployments.

Responsibilities

• Architect and build an end-to-end ML Platform-as-a-Product.
• Standardize notebooks, local CLI tools, and SDKs for repeatable model development.
• Develop scalable training pipelines using Vertex AI Pipelines, Kubeflow, and Databricks.
• Create CI/CD/CT pipelines to package models into scalable microservices on GKE or Vertex Endpoints.
• Implement observability, data/concept drift monitoring, and automated retrain triggers.
• Manage enterprise Feature Store, artifact registries, and metadata lineage.

Skills

Platform architecture

CI/CD

ML GItOps

Monitoring & observability

Tools

Vertex AI

GKE

Kubeflow

Databricks

Terraform

BigQuery

Google Cloud Storage

Job description

We are seeking a Senior MLOps Engineer to architect and build our next-generation, end-to-end ML Platform-as-a-Product. In this role, you will design the unified framework that

abstracts infrastructure complexities, enabling ML Engineers and Data Scientists to seamlessly write, train, deploy, and monitor models in production.

Key Responsibilities

ML Lifecycle Stage

Authoring & Local Dev

Standardize notebook environments, local CLI tools, and SDKs to make model writing highly repeatable and containerized from day one.

Training & Orchestration

Build automated, scalable training pipelines utilizing Vertex AI Pipelines, Kubeflow, and Databricks, integrating unified data access layers.

Construct robust CI/CD/CT (Continuous Training) pipelines that package models into auto-scaling microservices on Google Kubernetes Engine (GKE) or Vertex AI Endpoints.

Operations & Drift

Implement end-to-end observability frameworks to track system performance, monitor data/concept drift, and trigger automated alert-and-retrain loops.

Platform Governance

Manage the enterprise Feature Store, artifact registries, and unified metadata tracking (lineage) for compliance and governance.

Technical Requirements & Stack

• Platform Architecture: Proven experience building a unified "ML Platform-as-a- Service" inner-source framework for internal engineering consumption.
• GCP Ecosystem: Deep expertise in Vertex AI (Pipelines, Feature Store, Model Registry, Endpoints), GKE, BigQuery, and Google Cloud Storage.
• Infrastructure as Code (IaC): Strong hands-on experience using Terraform to provision secure, compliant, and cost-optimized ML infrastructure.
• Maturity Frameworks: Experience setting up end-to-end automation covering ML GitOps, automated testing, and zero-downtime canary deployments.

Key Takeaway

Your objective is simple: Reduce time-to-market for ML models from months to minutes by delivering a friction-free, standardized, and self-service engineering framework