Job Summary
MLOps Engineer CAREERS · AI & DATA ENGINEERING Join HCLTech's AI & Data practice to design, deploy, and operate production machine learning systems at scale — leveraging modern cloud-native tooling and a culture of continuous improvement. Type: Full-time, Permanent Cloud: GCP preferred ABOUT HCLTECH Model: Hybrid / Remote Level: Senior (5+ years) HCLTech is a global technology company, home to 225,000+ people across 60 countries, delivering industry-leading capabilities in engineering, technology, and digital transformation. We supercharge progress for the world's leading enterprises. WHAT YOU'LL DO Design, develop, and deploy machine learning models in production, delivering end-to end microservices-based solutions for both batch and real-time inference pipelines. Implement and optimise MLOps pipelines using open-source tools — Kubeflow, Seldon, MLFlow, Docker, and Kubernetes — with a focus on automation and reproducibility. Build and maintain tooling around monitoring, logging, automated testing, and performance testing to ensure pipeline health and model reliability. Partner closely with Data Scientists to streamline the ML model development lifecycle and continuously improve model performance. Ensure production models meet standards for scalability, maintainability, and robustness through thoughtful architecture and operational practices. Monitor and troubleshoot ML model performance and infrastructure issues in production; familiarity with Prometheus and Grafana is a strong advantage. Support and enhance the broader ML software infrastructure: CI/CD pipelines, data stores, cloud services, network configuration, security, and system monitoring. Leverage cloud technologies — primarily GCP — to optimise resource allocation and manage costs effectively. Continuously research emerging trends, tools, and best practices in MLOps and bring relevant innovations back to the team. TECHNOLOGY STACK Google Cloud Platform is preferred; equivalent experience on AWS or Azure is welcome. Cloud & Infrastructure GCP AWS MLOps & Pipelines Kubeflow Azure MLFlow Kubernetes Seldon Docker Vertex AI Terraform Airflow Observability Prometheus CI/CD & Development GitHub Actions POSITION DETAILS Department Employment Type Work Model Experience Level Cloud Platform Nice to Have HCLTech Grafana Jenkins ELK Stack Python AI & Data Engineering Full-time, Permanent Hybrid / Remote Cloud Monitoring FastAPI Senior — 5+ years in MLOps or ML Engineering GCP preferred; AWS or Azure accepted GCP Professional Certificate, CKA/CKAD, FinOps awareness