Senior ML Engineer Lead - Time Series

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 28,200+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Looking for a highly skilled “Senior Machine Learning Engineering Lead” to oversee and drive machine learning initiatives focusing on time series analysis, process curve analysis, tabular data, and feature engineering. In this role, you will lead a team of engineers and data scientists, ensuring the effective delivery of machine learning solutions to customers. You will also be responsible for designing efficient workflows, building robust CI/CD pipelines, and handling client interactions to deliver high-quality solutions on time.Roles & Responsibilities:

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).

Process Curve Analysis: Apply machine learning techniques to analyze process curves, optimize processes, and predict system behavior based on historical data.

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.

Leadership 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.

Required skills:Technical 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.

Cloud Platforms: Experience with cloud computing services like AWS, GCP, or Azure for model deployment and scalability.

Version Control: Proficient in using Git for version control and collaboration.

Soft Skills:

• Strong leadership and team management skills, with a focus on mentoring and development of team members.
• Excellent communication skills for handling customer interactions, explaining technical concepts to non-technical stakeholders, and delivering presentations.
• Problem-solving mindset with the ability to analyze complex data and identify actionable insights.
• Highly organized, detail-oriented, and able to manage multiple projects simultaneously.

Experience:

• 8+ years of experience in machine learning engineering with a focus on time-series analysis, process curve analysis, tabular data, and feature engineering.
• At least 3-5 years of leadership experience managing teams and handling customer-facing responsibilities.
• 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 mindset.

Preferred Expertise:

• Experience in deploying models at scale using containerization technologies like Docker and Kubernetes.
• Knowledge of MLOps principles and practices.
• Background in domain-specific areas (e.g., manufacturing, finance, healthcare) related to time-series and process data.

B.E./ M.E./M. Tech in Computer Science Engineering, Ph. D is plus

8 - 12 years