Company Overview :

Clovio IT Consulting Private Limited is a specialized technology services firm focused on delivering high-impact digital transformation and data-driven solutions. We partner with global enterprises to modernize their technical infrastructure, optimize data pipelines, and implement scalable machine learning frameworks. Our culture is built on technical excellence, collaborative problem-solving, and a commitment to engineering rigor, serving clients across the fintech, retail, and healthcare sectors.

Role Overview :

As an MLOps Engineer, you will bridge the gap between data science experimentation and production-grade software engineering. You will be responsible for designing and maintaining the end-to-end lifecycle of machine learning models, ensuring they are scalable, reliable, and performant. Working closely with data scientists, software developers, and infrastructure teams, you will build the automated systems that allow our clients to deploy, monitor, and manage complex AI models in real-world environments, directly influencing the business value derived from their data assets.

Key Responsibilities :

- Architect and implement robust CI/CD pipelines for machine learning models to ensure seamless integration and deployment across diverse environments.

- Design scalable data engineering workflows to automate the ingestion, transformation, and validation of large-scale datasets for model training.

- Manage containerized environments using Docker and Kubernetes to ensure consistency and portability of ML applications across development and production.

- Establish comprehensive monitoring and logging frameworks to track model performance, drift, and infrastructure health, enabling proactive maintenance.

- Collaborate with cross-functional teams to optimize model serving latency and resource utilization, ensuring cost-effective and high-performance delivery.

Required Skillset :

- Demonstrated expertise in building and maintaining MLOps infrastructure, with a deep understanding of the full machine learning lifecycle.

- Proficiency in Python for developing production-grade code, automation scripts, and data processing pipelines.

- Proven ability to manage containerization and orchestration tools, specifically Docker and Kubernetes, in complex cloud environments.

- Strong grasp of DevOps principles, including infrastructure-as-code and automated testing, to streamline deployment cycles.

- Excellent analytical and communication skills, with the ability to translate complex technical requirements into actionable engineering solutions for stakeholders.

- Ability to thrive in a remote-first environment, maintaining high levels of autonomy and collaborative engagement across distributed teams.

- A minimum of 5 - 15 years of professional experience in software engineering, data engineering, or machine learning operations.