Domain: EV Battery Manufacturing & Electrochemistry
Experience: 5–7 Years
Education: B. Tech/M. Tech in Chemical Engineering or M. Sc. Chemistry
Role Objective
We are seeking a high-calibre Senior Machine Learning Engineer (L5) to bridge electrochemical research and gigafactory-scale manufacturing.
This role demands a rare combination of:
Robust Chemistry / Chemical Engineering fundamentals
Advanced Machine Learning & Deep Learning expertise
Rigorous statistical and probabilistic thinking
You will build physics-informed digital twins to predict battery life, optimize manufacturing yield, and enable intelligent decision-making at scale.
Core Responsibilities
Advanced Machine Learning & Deep Learning
Design and deploy time-series models (Transformers, LSTMs) to analyze battery cycling and degradation patterns
Develop computer vision systems (CNNs, Vision Transformers) for defect detection in electrode coating and assembly
Build physics-informed models (PINNs) embedding electrochemical constraints into learning frameworks




Implement self-supervised and representation learning on large-scale industrial datasets
Generative AI & Intelligent Systems (good to have)
Develop RAG-based systems to extract insights from chemical literature, patents, and technical documents
Build agentic workflows / multi-agent systems for automated root-cause analysis across plant and lab data
Enable knowledge-driven AI systems linking process, material, and performance data
Statistical Modelling & Scientific Rigor
Lead Design of Experiments (DOE) for current materials and process optimization
Apply multivariate statistical analysis (ANOVA, MANOVA) to understand process variability
Develop probabilistic models (Gaussian Processes, Monte Carlo methods) for:
Remaining Practical Life (RUL)
Battery reliability and uncertainty quantification
Implement statistical quality control (CUSUM, EWMA) for early drift detection
Physics-Informed & Domain-Driven Modelling
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📌 Machine Learning Engineer Bengaluru (India)
🏢 Agratas – A Tata Enterprise
📍 India