Sr.Engineer - AI_ML

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 27,000+ 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.

Roles & Responsibilities :

About the Role

We are seeking an experienced Machine Learning Engineer to design, implement, and deploy advanced ML models for real-world applications. The role requires strong expertise in LLM fine-tuning, graph neural networks (GNNs), anomaly detection, SecureMLOps, and edge AI deployment. You will work on risk scoring models, model versioning, and secure ML pipelines, ensuring robustness, scalability, and compliance for production-grade systems.

Key Responsibilities

• Develop, fine-tune, and deploy ML and AI models using Python and PyTorch.

• Work on LLM fine-tuning and adaptation for domain-specific tasks.

• Implement graph neural networks (GNNs) for advanced data representation and analysis.

• Build and optimize anomaly detection systems for real-time applications.

• Design and maintain SecureMLOps pipelines for safe, compliant, and scalable ML deployment.

• Deploy models on edge devices, optimizing for performance and efficiency.

• Develop and maintain risk scoring models for predictive insights.

• Manage model versioning, monitoring, and lifecycle management in production.

• Collaborate with cross-functional teams (data scientists, software engineers, and domain experts) to deliver robust AI solutions.

Educational qualification:

B.E/B.Tech

Experience : 5-7

Mandatory/requires Skills :

• 5–7 years of experience in applied machine learning.

• Strong proficiency in Python and PyTorch.

• Hands-on experience in LLM fine-tuning and transformer-based models.

• Strong knowledge of GNNs (Graph Neural Networks).

• Experience with SecureMLOps frameworks, CI/CD, and ML lifecycle tools.

• Knowledge of edge deployment strategies for ML models.

• Experience in anomaly detection and risk scoring models.

• Familiarity with model versioning, monitoring, and scaling in production environments.

Good to Have

• Experience with cloud platforms (AWS, GCP, Azure) for ML deployment.

• Familiarity with containerization (Docker, Kubernetes).

• Knowledge of distributed training and optimization techniques.