Unify Technologies - MLOps Engineer

<p><b>Job Description :</b></p><p><br/></p><p>We are seeking a highly skilled MLOps Engineer Contractor to support the development and operation of automation tools and dashboards within our Machine Learning Platform and Search infrastructure.

This role is critical in ensuring the reliability, scalability, and efficiency of our ML inference services, CI/CD workflows, and training data pipelines.<br/><br/><b>Responsibilities :</b><br/><br/>Web UI Development :<br/><br/>- Design and implement web-based dashboards for real-time monitoring of ML inference services.<br/><br/>- Build intuitive web tools to manage and visualize CI/CD workflows and release processes for ML Search infrastructure.<br/><br/>Model Lifecycle Management :<br/><br/>- Standardize and automate the onboarding of new ML models into the existing platform.<br/><br/>- Develop interfaces for training automation, versioning, and model deployment workflows.<br/><br/>CI/CD & Deployment Operations :<br/><br/>- Maintain and improve CI/CD pipelines for ML and Search services.<br/><br/>- Operate and optimize deployment processes, including daily release sign-offs and rollback mechanisms.<br/><br/>Big Data Pipeline Management :<br/><br/>- Manage and enhance training data pipelines using modern big data tools and frameworks (e.g., Spark, Hadoop, Airflow).<br/><br/>- Focus on cost-efficiency, scalability, and high reliability for data ingestion, transformation, and storage.<br/><br/><b>Required Qualifications :</b><br/><br/>- 5- 10 years of hands-on experience in MLOps, DevOps, or related roles.<br/><br/>- Strong proficiency in Python, Bash, or similar scripting languages.<br/><br/>- Experience building web-based dashboards or internal tools (React.js, Flask, FastAPI, or similar).<br/><br/>- Deep understanding of CI/CD tools and workflows (e.g., Jenkins, GitHub Actions, GitLab CI/CD, Argo).<br/><br/>- Familiarity with ML model deployment, version control, and model serving tools (e.g., MLflow, SageMaker, Kubeflow, TorchServe).<br/><br/>- Experience with big data technologies (e.g., Apache Spark, Hadoop, Airflow, AWS Glue).<br/><br/>- Solid understanding of containerization and orchestration (Docker, Kubernetes, Fargate, etc.).</p> (ref:hirist.tech)