Applied ML - Engineer

Job Description :

Development, adaptation, and implementation of AI / ML algorithms and frameworks, Prediction algorithms

Developing deep learning and machine learning algorithms (CNN, object detection, segmentation, SVM, AE)

Time series forecasting : AR, ARIMA, SARIMA, ES, Prophet, LSTM

Conduct data preprocessing, augmentation, and annotation workflows for image datasets.

Design, train, and validate deep learning architectures for feature identification using CNN, ResNet,

EfficientNet, YOLO, U-Net, Mask R-CNN, ViT / Swin Transformer.

Develop clean, modular, and production-ready code for model training, inference, and deployment.

Collaborate with domain experts to translate agricultural knowledge into AI models.

Support integration of models with mobile application (through APIs and deployment-ready formats like TensorFlow Lite / ONNX).

Write unit tests, integration tests, and documentation to support long-term use of the framework.

Document methodologies, benchmarking reports, and prepare technical handover materials.

Minimum Qualifications and Experience :

B.Tech in Computer Science, Electronics and Communications or any related field with 3-5 years of relevant experience

OR

M.Tech in Computer Science, Electronics and Communications or any related field with 2-3 years of relevant experience

Required Expertise :

Strong hands-on experience with Python and ML / DL frameworks (PyTorch, TensorFlow, Keras).

Proficiency in computer vision techniques – CNNs, object detection (YOLO / SSD), segmentation (U-Net / Mask R-CNN), Vision Transformers (ViT, Swin Transformer, DeiT).

Libraries : NumPy, Pandas, OpenCV, Scikit-learn, Matplotlib / Seaborn.

Knowledge of model optimization for deployment (quantization, pruning, TensorFlow Lite, ONNX).

Experience in developing APIs (Flask / FastAPI) for model serving.

Familiarity with ETL processes, data pipelines, and statistical validation methods.

Basic understanding of Docker and version control (Git) and experience with MLOps tools

Ability to write production-grade Python code following best practices (modular design, logging, testing, error handling)

Understanding of statistical analysis such as normality test, dicky fuller test etc

Location of work :

TIH-IoT, IIT Bombay Campus, Powai, Mumbai 400076.