Join Bosch Global Software as Senior ML Lead – Time Series Analytics Expert
Senior Machine Learning Engineering Lead – Time Series | Bosch Global Software Technologies, Bengaluru
Location: No.123, Industrial Layout, Hosur Road, Koramangala, Bengaluru, India
Position Type: Full-Time
Department: Machine Learning / Artificial Intelligence
Company: Bosch Global Software Technologies Private Limited
About Bosch Global Software Technologies
Bosch Global Software Technologies (BGSW) is a wholly owned subsidiary of Robert Bosch GmbH, a globally respected technology and services provider. With over 28,200 professionals, BGSW is the largest software development center for Bosch outside Germany. It serves as the innovation hub for the Bosch Group, delivering cutting-edge solutions across Engineering, IT, and Business Services. Headquartered in India, BGSW operates globally with a strong presence in the United States, Europe, and the Asia-Pacific region.
As a technology powerhouse, Bosch Global Software Technologies is committed to driving future-ready digital transformation and enabling smarter, connected, and efficient ecosystems across industries. If you’re passionate about solving real-world challenges using advanced machine learning, this is the place to be.
Position Overview
We are looking for a dynamic and visionary Senior Machine Learning Engineering Lead – Time Series to spearhead our data science initiatives with a focus on time-series modeling, process curve analytics, and tabular data management. This role is critical in leading high-impact AI/ML projects and delivering end-to-end solutions, from data engineering and feature design to production-ready model deployment.
You will lead a team of skilled engineers and data scientists, design intelligent workflows, ensure robust CI/CD integration, and maintain strong client partnerships. The position is ideal for someone with a solid ML background, proven leadership in delivering business-focused AI solutions, and hands-on experience in time-series and tabular data analytics.
Key Responsibilities
1. Machine Learning and Data Engineering
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Time Series Forecasting & Anomaly Detection:
Architect and deploy machine learning models tailored to time-series data for predicting trends, detecting anomalies, and optimizing business operations. -
Process Curve Analytics:
Leverage statistical and ML techniques to analyze process curves, enabling predictive maintenance and process optimization in manufacturing and industrial setups. -
Tabular Data Analysis:
Work with structured datasets to extract insights and train predictive models that support strategic decision-making. -
Feature Engineering:
Develop high-impact features aligned with domain-specific requirements to boost model accuracy and generalization. -
Model Development:
Design, test, and refine scalable machine learning algorithms that address diverse use cases and business demands.
2. Leadership & Team Management
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Team Supervision & Mentoring:
Lead a multidisciplinary team of ML engineers and data scientists. Provide hands-on technical guidance, career mentoring, and regular performance reviews. -
Cross-Functional Collaboration:
Work with software developers, DevOps, domain experts, and product managers to align AI solutions with business goals. -
Client Engagement:
Act as the technical liaison for customers, understanding project objectives, translating them into technical deliverables, and ensuring successful outcomes. -
Milestone Delivery:
Drive project timelines, oversee deliverables, and ensure solutions meet customer expectations in terms of quality, accuracy, and efficiency.
3. Workflow Automation & Deployment
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CI/CD Integration:
Build robust CI/CD pipelines for automating the training, validation, and deployment of machine learning models using tools like Jenkins, GitLab, or CircleCI. -
Model Deployment & Monitoring:
Manage deployment of ML models in real-world environments, ensuring performance consistency, reliability, and scalability. -
Automated Data Pipelines:
Design automated workflows that streamline data ingestion, transformation, and model training with seamless integration into enterprise systems.
4. Quality Assurance and Optimization
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Performance Monitoring:
Continuously track model performance metrics post-deployment. Troubleshoot issues related to drift, latency, or accuracy. -
Process Improvement:
Drive innovation by refining machine learning lifecycle processes, reducing development bottlenecks, and shortening time-to-market. -
Documentation & Reproducibility:
Maintain comprehensive documentation to support model reproducibility, auditing, and future development.
Technical Skills Required
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Languages & Frameworks:
Proficiency in Python, R, or related programming languages. Strong experience in ML libraries such as TensorFlow, PyTorch, Keras, scikit-learn, XGBoost. -
Time Series Tools:
Expertise in time-series forecasting models including ARIMA, LSTM, Prophet, and anomaly detection frameworks. -
Data Engineering & Manipulation:
Strong command of NumPy, Pandas, SQL, and data wrangling tools. Experience working with large-scale datasets and ETL pipelines. -
Feature Engineering:
Deep understanding of feature creation techniques that enhance predictive accuracy and interpretability. -
Cloud Platforms:
Hands-on experience with AWS, Google Cloud Platform (GCP), or Microsoft Azure for model training and deployment. -
Version Control:
Skilled in Git for code versioning, collaboration, and release management. -
CI/CD & DevOps:
Working knowledge of CI/CD tools such as Jenkins, CircleCI, GitHub Actions for ML automation pipelines.
Soft Skills & Leadership Traits
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Strong analytical mindset and problem-solving abilities
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Excellent communication skills to explain complex concepts to stakeholders
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Proactive leadership style with a track record of mentoring junior professionals
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Capable of managing multiple projects while maintaining high-quality output
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Collaborative and adaptable in cross-functional, multicultural environments
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Customer-focused with a strong sense of accountability
Experience & Qualifications
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Experience Required:
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8+ years of experience in Machine Learning Engineering or Applied Data Science
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At least 3–5 years of experience leading teams and managing client relationships
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Hands-on experience in deploying ML models in production environments
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Experience in handling real-world time-series and process data
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Track record of delivering solutions in enterprise settings or international projects
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Educational Background:
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Bachelor’s or Master’s degree (B.E., M.E., M.Tech) in Computer Science, Data Science, or related fields
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Ph.D. in a relevant field is a strong plus
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Preferred Expertise (Nice to Have)
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MLOps Expertise:
Familiarity with MLOps pipelines, monitoring frameworks, and lifecycle management best practices. -
Containerization & Orchestration:
Experience using Docker and Kubernetes for scalable model deployment and containerized environments. -
Domain Knowledge:
Exposure to industries like manufacturing, automotive, healthcare, or finance, especially with time-series/process data.
Why Join Bosch Global Software Technologies?
At BGSW, innovation is not just a buzzword—it’s a way of life. As a part of one of the world’s most respected engineering brands, you’ll have the opportunity to work on cutting-edge technologies with global impact.
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Global Exposure: Work on international projects with teams across the US, Europe, and Asia-Pacific
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Innovation Culture: Contribute to next-gen solutions at the forefront of machine learning and AI
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Career Growth: Benefit from structured leadership programs and learning platforms
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Inclusive Work Environment: Thrive in a workplace that values diversity, equity, and continuous improvement
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Work-Life Balance: Enjoy flexible work arrangements and employee wellness initiatives
Ready to Make a Difference?
If you’re an experienced ML engineer passionate about time-series modeling and data science leadership, and are looking to drive innovation in a global setting, we want to hear from you.
Apply now and take the next big step in your career with Bosch Global Software Technologies.