We are looking for Senior Data Scientist with hands-on experience in developing AI/ GenAI solutions, and strong expertise in leading AI tools and packages. The candidate must be proficient in designing and deploying AI models, applying GenAI techniques, and implementing DevOps/MLOps practices to ensure scalable and reliable deployment.
Responsibilities:
Translate business requirements into AI solutions; conduct feasibility studies and align machine learning, deep learning, and advanced analytics models with business stakeholder requirements.
Design and build machine learning and deep learning models for predictive analytics, automation, and decision support
Apply supervised and unsupervised learning techniques, along with prescriptive analytics, to optimize models and recommend optimal actions
Develop and fine tune GenAI applications (LLMs, diffusion models, multimodal systems) for industrial applications
Deploy AI/ML models on Azure, leverage cloud services for scalability, monitoring, and integration with enterprise systems
MLOps & DevOps Practices - Establish CI/CD pipelines, automate model training, testing, and deployment, monitor performance in production
Evaluate models using metrics, optimize hyperparameters, and ensure robustness, fairness, and reliability.
Work closely with cross‑functional teams (engineering, product, operations); mentor junior data scientists and share best practices.
Engineering Graduate/ Post Graduation (ME/MTech./MS/MBA) with a minimum of 6 years exp.
Developed and delivered a comprehensive portfolio of AI projects, leveraging advanced AI/ML techniques towards mechanical engineering solutions including predictive maintenance, process optimization, real-time prediction
Experience with developing GenAI applications in industrial contexts: LLM-based copilots for technicians, automated report generation, and multimodal diagnostics.
Skilled in writing efficient, reusable Python code leveraging leading libraries such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face
Hands-on experience developing advanced ML models for industrial applications, with a strong focus on using computer vision techniques for object detection, defect recognition, and visual anomaly detection
Data Engineering & Preprocessing - Collect, clean, and transform large datasets; apply feature engineering and ensure data quality for AI pipelines.
Multimodal Data Analysis - Ability to analyze and derive insights from diverse data types, including structured and unstructured formats such as images, videos, and text.
Proficiency with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes) to streamline the model lifecycle
Familiarity with surrogate model fundamentals, Edge computing applied to mechanical systems and physics‑informed machine learning
Familiarity with Model-Based Systems Engineering (MBSE) tools (e.g., MATLAB, Simulink, ANSYS) and their integration with AI workflows.
Fundamental understanding of mechanical principles including machine design, vibrations, thermodynamics, fluid mechanics, product quality assessment, material science etc.
Certification from leading providers like Microsoft. Google. IBM. AWS (e.g.. Azure Al Fundamentals. Azure Solutions Architect)
Understanding of data and model security best practices in cloud environment
Experience in creating and publishing whitepapers/ blogs/ registering for IP rights/ patents
Challenging Work Environment: We provide a stimulating environment where you will have the opportunity to work on complex and meaningful projects.
Global Impact: Be part of a multinational organization where your work and ideas contribute to both local and global success.
Growth & Development: We are dedicated to helping you develop your career by providing opportunities to grow, take on new challenges, and lead initiatives that matter.
Innovation Encouraged: Our culture supports challenging the "Status Quo" and fostering new ideas to build tools, frameworks, and applications that make a difference.
Collaboration & Support: We work in teams where each person is valued, and where collaboration, mentoring, and support are at the heart of our success.
This role offers a hybrid working arrangement, allowing you to split your time between working remotely and being on-site at our Atlas Copco Group in Pune, India (IN).
Talent Acquisition Team: Divya Prafull Kapade
