Data Engineer

Company Overview Pinnacle Infotech values inclusive growth in an agile, diverse environment. With 30+ years of global experience, 3,400+ experts completed 15,000+ projects across 43+ countries for 5,000+ clients. Join us for rapid advancement, cutting-edge training, and impactful global projects. Embrace E.A.R.T.H. values, celebrate uniqueness, and drive swift career growth with Pinnaclites! Job Title: MLOps Engineer Job Summary: As an MLOps Engineer, you will be responsible for building, deploying, and maintaining the infrastructure required for machine learning models and ETL data pipelines. You will work closely with data scientists, and software developers to streamline our machine learning operations, manage data workflows, and ensure that the ML solutions are scalable, reliable, and secure. Location- Durgapur/Jaipur/Madurai Qualifications: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. 3+ years of experience in MLOps, data engineering, or a similar role. Proficiency in programming languages such as Python, Spark, and SQL. Experience with ML model deployment frameworks and tools (e.g., MLflow). Hands-on experience with cloud platforms (AWS, Azure, GCP) and infrastructure management. Familiarity with containerization and orchestration tools (Docker, Kubernetes). Understanding of DevOps practices, CI/CD pipelines, and monitoring tools. Excellent problem-solving skills and the ability to work independently and as part of a team. Key Responsibilities: Data Engineering and Pipeline Management: Design, develop, optimize, and maintain ETL processes and data pipelines to collect, process, and store data from multiple sources. Ensure data quality, integrity, and consistency across various databases. Collaborate with data scientists to make data available in the right format and speed for machine learning. Implement and manage data security and privacy protocols as per industry standards. ML Operations and Deployment: Design, build, and optimize scalable and reliable ML deployment pipelines. Develop CI/CD pipelines for automated ML model training, testing, and deployment. Implement and manage containerization (Docker) and orchestration tools (e.g., Kubernetes) for ML workflows. Monitor and troubleshoot model performance and infrastructure, ensuring smooth operation in production environments. Infrastructure Management: Manage cloud infrastructure (AWS, GCP, Azure) to support data and ML operations. Optimize and scale ML and data workflows to handle large-scale datasets. Set up and manage monitoring tools for infrastructure and application performance. Collaboration and Best Practices: Work closely with data science, software development, and product teams to understand project needs and optimize model performance. Develop and document best practices, guidelines, and protocols for ML lifecycle management. Interested candidates, please share your resume at sunitas@pinnacleinfotech.com Show more Show less