MLOps Engineer

Job Overview

TriniTech Consulting Ltd is looking for an experienced MLOps Engineer to spearhead the creation and operation of scalable, data-driven, and production-level machine learning systems. The successful candidate will be accountable for managing the entire ML deployment pipeline, including model version control, deployment, continuous performance tracking, automation via CI/CD, and retraining processes.

Key Responsibilities

• Design, build, and maintain production-grade ML systems with focus on scalability and reliability.
• Manage the entire ML model lifecycle from versioning to deployment, monitoring, and retraining workflows.
• Implement CI/CD pipelines for seamless model deployment and updates.
• Collaborate closely with product managers, data scientists, and data engineering teams to align ML deployment with business needs.

Required Skills and Qualifications

• Comprehensive understanding of MLOps methodologies including deployment, version control, monitoring, and retraining.
• Strong programming skills in Python alongside expertise in ML libraries such as PyTorch, Scikit-learn, and XGBoost.
• Hands-on experience with CI/CD utilities like Jenkins and GitHub Actions.
• Proficiency with cloud services, particularly AWS environments.
• Experience with ML training and inference platforms like Databricks and Cloudera.
• Familiarity with MLOps orchestration tools such as MLflow and Kubeflow, including cloud-native deployment techniques.
• Proficient in developing distributed data processing pipelines using Apache Spark (PySpark, Scala, Java), Hadoop, and Kafka.
• Excellent communication and teamwork capabilities coupled with a proactive and self-driven approach to ownership.