Sr. ML Engineer Expert - Time Series
- Bangalore, Karnataka
- Permanent
- Full-time
Machine Learning and Data Engineering:Time Series Analysis: Develop and implement advanced machine learning models for analyzing time-series data (e.g., forecasting, anomaly detection).Tabular Data: Manage and work with structured/tabular datasets to build models that deliver actionable insights.Feature Engineering: Design and implement innovative feature engineering techniques to enhance model performance, ensuring that features align with business goals.Model Development and Optimization: Develop, test, and optimize machine learning models and algorithms for various business use cases.Deep-Learning -LLM & RAGAgentic-AI FrameworksLeadership and Team Management:Team Mentorship: Lead a team of machine learning engineers and data scientists, providing guidance and mentorship to junior team members.Collaboration: Work closely with data scientists, software engineers, product managers, and other stakeholders to design, implement, and deliver end-to-end solutions.Customer Handling: Serve as the primary point of contact for customers, gathering requirements, addressing technical challenges, and ensuring the timely delivery of high-quality solutions.Client Deliverables: Ensure all project milestones are met, and machine learning models and solutions are aligned with customer expectations.Pipeline and Workflow Design:CI/CD Pipeline: Design and maintain robust CI/CD pipelines for machine learning model training, validation, and deployment, ensuring efficient and automated workflows.Model Deployment and Monitoring: Oversee the deployment of machine learning models into production, ensuring they meet performance, reliability, and scalability requirements.Automated Workflows: Build automated workflows for data pipelines, model training, evaluation, and reporting, ensuring seamless integration with business processes.Quality Assurance and Optimization:Performance Monitoring: Monitor model performance post-deployment, identifying and addressing any issues related to accuracy, speed, or scalability.Process Improvement: Continuously evaluate and improve model development practices, machine learning pipelines, and workflows to drive efficiency and reduce time-to-market.Documentation: Ensure that all models, pipelines, and processes are well-documented and easily reproducible for future iterations or modifications.QualificationsEducational qualification:Bachelor's or master's degree in Computer science, Engineering, Mathematics, or a related field (Ph.D. is a plus).Experience:10+ total with 8+ years of experience in machine learning engineering with a focus on time-series analysis, process curve analysis, tabular data, and feature engineering.Strong experience in designing and deploying ML models in production environments.Proven track record of successfully managing client relationships and delivering high-quality solutions on time.Experienced in working in cross-functional, international setups.Entrepreneurial, business-driven mindsetMandatory/requires Skills:Programming Languages: Proficiency in Python, R, or other relevant languages (e.g., Java, Scala).Machine Learning Frameworks: Expertise in ML libraries like scikit-learn, TensorFlow, Keras, XGBoost, PyTorch, etc.Time Series Analysis: Experience with time-series forecasting models (ARIMA, LSTM, Prophet, etc.) and anomaly detection.Data Engineering: Expertise in working with large-scale datasets and tools like Pandas, NumPy, SQL, and data wrangling techniques.Feature Engineering: Strong skills in creating meaningful features to improve model accuracy and performance.CI/CD Tools: Experience with CI/CD tools like Jenkins, GitLab, CircleCI, or similar platforms for automating deployment workflows.