Job Description

Company Description

Digizac Solutions is an ISO Certified dynamic and innovative IT company based in Pune. We provide top-notch IT hardware and software solutions to corporate clients, tailored to their unique requirements. Our offerings include hardware procurement and installation, software development and implementation, network maintenance and management, cyber security solutions, cloud computing services, data management, and annual maintenance contracts. We strive to build long-term relationships with our clients by providing timely support and superior customer service.

Role Description

Summary:

It's a unique role for an individual passionate about both the operational and scientific aspects of machine learning. Your primary focus (approx. 80%) will be on designing, building, and maintaining our robust MLOps infrastructure on Google Cloud Platform (GCP), with a strong emphasis on the Vertex AI suite. The rest of your time (approx. 20%) will involve hands-on data science work, including model refinement, feature engineering, and training, ensuring a seamless transition from research to production. This role will be a critical link between our data science and engineering teams, responsible for the entire lifecycle of ML models.

Responsibilities:

Architect and build the end-to-end MLOps infrastructure using GCP services. Leverage Vertex AI Pipelines, Experiments, and Model Registry to create reproducible and governable ML workflows. Create and manage CI/CD pipelines using tools like Cloud Build, Jenkins, or GitLab CI for the automated building, testing, containerization, and deployment of ML models to Vertex AI Endpoints. Implement automated systems for deploying new model versions to Vertex AI Endpoints for both online and batch predictions. Configure and manage auto-scaling to handle variable demand efficiently. Package ML models and dependencies into Docker containers and manage their deployment and lifecycle within Google Kubernetes Engine (GKE). Implement and manage all cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform or CloudFormation to ensure consistency, reproducibility, and version control. Implement robust monitoring using Cloud Monitoring and Cloud Logging. Analyse and optimize model serving infrastructure for performance, scalability, and cost-efficiency. Implement security best practices for protecting models, data, and infrastructure using GCP IAM, service accounts, and VPC service controls. Work within Vertex AI Workbench (managed notebooks) to refine, test, and package models developed by the data science team, ensuring they meet production performance and code quality standards. Collaborate on designing and implementing feature engineering pipelines using BigQuery, Dataflow, and the Vertex AI Feature Store to create a centralized repository of reusable, production-ready features. Utilize Vertex AI Training to run custom training jobs at scale. Leverage the hyperparameter tuning service to optimize model performance. Act as the primary technical liaison between data scientists and the data platform team. Translate research models and notebooks into robust, production-ready code and components for ML pipelines.

Qualifications:

Bachelor's or master's degree in computer science, Data Science, Statistics, or a related quantitative field. 3+ years of hands-on experience in a data science role, building and shipping machine learning models to production. Strong understanding and hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and their services for machine learning. Extensive experience with containerization (e.g., Docker) and container orchestration (e.g., Kubernetes). Proficiency in Infrastructure as Code (IaC) tools (e.g., Terraform, CloudFormation). Experience with CI/CD tools and practices (e.g., Git, Jenkins, GitLab CI). Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack). Solid understanding of SQL and experience working with large-scale data warehouses like Google BigQuery. Excellent problem-solving skills and the ability to work independently and in a team environment. Strong communication and collaboration skills.

Preferred Qualifications:

Google Cloud Professional Machine Learning Engineer or Professional Cloud DevOps Engineer certification. Strong understanding of networking and security concepts. Experience with advanced model serving concepts like custom prediction routines in Vertex AI or KServe on GKE. Familiarity with large-scale (petabyte-scale) data processing frameworks.
Job Type: Full-time

Pay: ₹1,000,000.00 - ₹1,800,000.00 per year

Benefits:

Provident Fund
Shift:

Day shift
Education:

Diploma (Required)
Experience:

IT: 2 years (Preferred) total work: 2 years (Preferred)
Work Location: In person