Senior Machine Learning Engineer Lead – Time Series | Bosch Global Software Technologies | Career Opportunity in Bengaluru
Senior Machine Learning Engineer Lead – Time Series Analysis | Bosch Global Software Technologies | Bengaluru
Company Overview
Bosch Global Software Technologies Private Limited, a fully owned subsidiary of Robert Bosch GmbH, has emerged as a global frontrunner in technology and services. As the largest Bosch development center outside Germany, with over 28,200+ associates, Bosch Global Software Technologies exemplifies engineering excellence and innovation. Based in Bengaluru, India, the company boasts a strong international footprint spanning the United States, Europe, and the Asia Pacific, delivering end-to-end Engineering, IT, and Business Solutions.
Position: Senior Machine Learning Engineer Lead – Time Series
Location: No. 123, Industrial Layout, Hosur Road, Koramangala, Bengaluru – 560095, India
Employment Type: Full-time
Role Overview
We are seeking an accomplished Senior Machine Learning Engineer Lead specializing in Time Series Analysis to drive and manage advanced machine learning initiatives. This role is pivotal in delivering innovative solutions through time series modeling, process curve analysis, and tabular data engineering. The ideal candidate will possess profound technical expertise, a strategic leadership mindset, and the ability to foster strong customer relationships while managing a high-performing team.
Key Responsibilities
Machine Learning and Data Engineering Expertise
- Time Series Modeling: Develop and implement sophisticated models for time series forecasting, anomaly detection, and trend analysis.
- Process Curve Analysis: Apply machine learning algorithms to analyze and optimize process curves, facilitating predictive insights and system optimization.
- Structured Data Handling: Manage and manipulate large-scale tabular datasets to construct predictive models yielding actionable business insights.
- Feature Engineering: Innovate and execute advanced feature engineering methodologies to enhance model performance, ensuring alignment with business objectives.
- Model Development & Optimization: Design, develop, test, and fine-tune machine learning models tailored to diverse business applications.
Leadership and Team Development
- Team Leadership: Guide and inspire a team of machine learning engineers and data scientists, cultivating an environment that values collaboration, innovation, and continuous professional development.
- Cross-functional Collaboration: Partner with data scientists, software developers, and product managers to architect and deliver comprehensive end-to-end solutions.
- Customer Engagement: Act as the primary technical liaison for clients, effectively gathering requirements, addressing challenges, and ensuring delivery of superior machine learning solutions.
- Project Management: Oversee project lifecycles, ensuring milestones are achieved, deliverables meet client expectations, and solutions maintain high standards of quality and innovation.
Pipeline and Workflow Engineering
- CI/CD Pipeline Management: Design and maintain robust Continuous Integration/Continuous Deployment pipelines to streamline model training, validation, and deployment.
- Model Deployment & Monitoring: Supervise the deployment of machine learning models into scalable production environments, ensuring operational excellence.
- Automation of Workflows: Create automated workflows for data ingestion, model training, evaluation, and reporting, ensuring seamless integration with business processes.
Quality Assurance and Continuous Improvement
- Performance Monitoring: Implement ongoing monitoring strategies for deployed models to maintain and enhance performance metrics such as accuracy and scalability.
- Process Enhancement: Regularly refine model development methodologies, machine learning pipelines, and workflow efficiencies to expedite time-to-market.
- Comprehensive Documentation: Maintain thorough documentation of all models, pipelines, and processes to support reproducibility and future enhancements.
Required Skills and Qualifications
Technical Proficiencies
- Programming Languages: Advanced proficiency in Python and R; familiarity with Java and Scala is advantageous.
- ML Frameworks and Libraries: Deep expertise with leading machine learning frameworks and libraries such as scikit-learn, TensorFlow, Keras, PyTorch, and XGBoost.
- Time Series Analytics: Hands-on expertise in time series forecasting techniques such as ARIMA, LSTM networks, and Prophet.
- Data Engineering: Mastery in handling large datasets using Pandas, NumPy, SQL, and advanced data wrangling techniques.
- Feature Engineering: Proven capabilities in developing sophisticated feature engineering strategies to optimize model outputs.
- CI/CD Tools: Extensive experience with Continuous Integration and Deployment tools including Jenkins, GitLab CI, and CircleCI.
- Cloud Platforms: Hands-on experience with AWS, Google Cloud Platform (GCP), or Microsoft Azure for model deployment and scalability.
- Version Control: Strong command over Git for source code management and collaboration.
Soft Skills
- Exemplary leadership, mentoring, and team management skills.
- Superior communication abilities to articulate complex technical concepts to diverse stakeholders.
- Exceptional analytical and problem-solving acumen, with an emphasis on deriving actionable insights from data.
- Highly organized and adept at managing multiple high-priority projects concurrently.
Experience Requirements
- A minimum of 8 years’ experience in machine learning engineering, particularly in time series analysis, process curve evaluation, and structured data modeling.
- 3-5 years’ leadership experience, including team management and direct client interaction.
- Proven expertise in designing, developing, and successfully deploying machine learning models within live production environments.
- Proven record of managing and nurturing client relationships and ensuring high-caliber solution delivery.
- Experience working in multinational, cross-functional teams.
- Entrepreneurial and business-centric approach to problem-solving and innovation.
Preferred Expertise
- Hands-on experience with containerization technologies such as Docker and Kubernetes for scalable model deployment.
- Familiarity with MLOps practices and frameworks.
- Domain expertise in industries like manufacturing, finance, or healthcare, particularly relating to time series and process data.
Educational Qualifications
- Bachelor’s or Master’s degree (B.E./B.Tech/M.E./M.Tech) in Computer Science, Information Technology, or a related field.
- Doctorate (Ph.D.) in a relevant domain is highly desirable.
Additional Information
- Experience Range: 8 – 12 years
- Location: Bengaluru, India
Why Join Bosch Global Software Technologies?
Joining Bosch Global Software Technologies means becoming part of a forward-thinking organization dedicated to excellence and innovation. Here, you will collaborate with some of the brightest minds in the industry, gain exposure to cutting-edge technologies, and contribute to impactful projects that shape industries globally. Bosch fosters a culture of continuous learning, inclusivity, and career growth, providing an ideal environment for ambitious professionals to thrive.
Keywords: Senior Machine Learning Engineer Lead, Time Series Analysis, Process Curve Analysis, Feature Engineering, CI/CD Pipelines, ML Model Deployment, AWS, GCP, Azure, Docker, Kubernetes, MLOps, Data Engineering, Bosch Careers, Machine Learning Jobs Bengaluru, Data Science Lead Roles, AI and ML Leadership Positions, Python, TensorFlow, Keras, PyTorch, scikit-learn, XGBoost.
Meta Description:
Explore an exciting opportunity at Bosch Global Software Technologies for a Senior Machine Learning Engineer Lead specializing in Time Series Analysis. Lead innovative projects, manage cross-functional teams, and drive high-impact machine learning solutions. Apply now to elevate your career in Bengaluru!