MLOps Engineer

About Gradera

Gradera defines a new category of enterprise transformation called Software-Orchestrated Services™ - where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed outcomes at scale. As an AI Native Services firm, we help enterprises redesign how work gets done across operations, product, engineering, customer experience, data, and enterprise workflows to move beyond fragmented AI pilots and disconnected automation toward measurable business outcomes.

We are seeking a high-caliber Build & Release Engineer – MLOps to join our Security & DevSecOps squad. In this role, you will automate machine learning pipelines and model deployment frameworks. You will be the technical anchor for the MLOps lifecycle, ensuring that our simulation-ready AI models are built, deployed, and monitored to the highest standards of engineering excellence. We are looking for an expert in Databricks and OpenShift who can work independently to automate the journey from experimentation to production.

Our Core MLOps Stack

· Databricks ML Pipelines for automated training and feature engineering

· MLflow for model tracking, versioning, and deployment on OpenShift

· OpenShift AI / Red Hat OpenShift Data Science (RHODS) for managed AI workloads

· Databricks Jobs & Workflows for scheduling and orchestrating complex data tasks

Containerization & Platform

· OpenShift Container Platform (OCP) and OpenShift Operators specialized for ML/GPU workloads

· Docker / Podman for containerizing model serving environments

· Python for automation, scripting, and ML pipeline development

CI/CD & Monitoring

· CI/CD for ML (GitOps) to automate model promotion across environments

· Model Monitoring (Evidently, Prometheus, or Grafana) for tracking drift and performance

· Red Hat Quay for secure storage of ML-specific container images

· Convert architectural blueprints into detailed technical designs for ML pipelines, covering data ingestion, training orchestration, and model serving.

· Databricks Automation: Independently design and manage Databricks Workflows to automate the end-to-end ML lifecycle, ensuring high reliability of data-heavy simulation models.

· Model Serving on OpenShift: Architect the deployment of models into OpenShift environments using MLflow and OpenShift AI, ensuring scalability and low-latency inference.

· ML Infrastructure as Code: Use OpenShift Operators to manage specialized ML resources (such as GPU nodes) and environment configurations autonomously.

· CI/CD for Machine Learning: Build and maintain automated pipelines that handle model versioning, testing, and seamless promotion from Databricks to OpenShift production clusters.

· Model Observability: Implement comprehensive Model Monitoring solutions to detect data drift and model decay, ensuring the digital twin's predictions remain accurate.

· Autonomous Execution: Lead the release management process for AI/ML features, resolving complex integration issues between Databricks and OpenShift without constant supervision.

· Security & Compliance: Ensure all ML artifacts (models, data, images) are signed, scanned, and deployed according to the Security & DevSecOps squad's governance standards.

· 6 to 8 years of experience in DevOps or MLOps, with a heavy focus on Databricks and OpenShift.

· Proven track record of automating MLflow deployments and managing Databricks Jobs.

· Strong proficiency in Python for developing ML pipelines and automation scripts.

· Hands-on experience with Red Hat OpenShift AI (RHODS) or similar cloud-native AI platforms.

· Deep understanding of CI/CD principles applied specifically to machine learning (MLOps).

· Demonstrated ability to work independently to solve complex infrastructure and data-flow bottlenecks.

· Experience deploying Digital Twin or Simulation models that require real-time inference.

· Familiarity with Kubeflow or OpenShift-native ML operators.

· Knowledge of big data technologies (Spark/Delta Lake) as they relate to ML training.

· Experience working for global product-led organizations.

· Exposure to industrial domains such as Manufacturing, Logistics, or Transportation is a plus.