RMSI Cropalytics is seeking a highly skilled Senior ML / AI Engineer with strong expertise in machine learning, statistical modelling, and applied AI for agriculture and geospatial analytics. The candidate will be responsible for designing, developing, training, fine-tuning, and deploying ML/AI models for crop forecasting, satellite image analysis, crop-stage classification, disease detection, and document intelligence.

Key Responsibilities

- Understand business and analytical requirements and translate them into scalable ML/AI solutions
- Design, implement, and optimize machine learning and deep learning models from concept to production
- Develop end-to-end ML pipelines, including data ingestion, preprocessing, feature engineering, training, validation, and deployment
- Fine-tune hyperparameters and optimize model performance using statistical and mathematical techniques
- Work extensively with satellite imagery (optical & SAR) for crop health, yield estimation, and disease detection
- Build forecasting models for crop yield, acreage estimation, weather impact, and time-series analysis
- Develop ML-based solutions for PDF/document parsing, OCR, and text extraction
- Deploy models on AWS/GCP using managed ML services and MLOps best practices
- Collaborate with data engineers, GIS experts, and domain specialists

Required Technical Skills

Programming & Core Libraries

- Expert-level programming in Python
- Strong hands-on experience with:
1. NumPy, Pandas, SciPy
2. Scikit-learn
3. Matplotlib, Seaborn, Plotly
4. Statsmodels

Machine Learning & Deep Learning Frameworks





- TensorFlow / Keras
- PyTorch
- AWS SageMaker (BlazingText, XGBoost, built-in algorithms)
- Hugging Face Transformers
- ONNX (model optimization and portability)

Must-Have Experience in Forecasting & Time-Series Models (Agriculture & Climate)

- ARIMA / SARIMA
- LSTM / GRU
- Temporal CNN
- Transformer-based time-series models
- XGBoost / LightGBM for yield prediction
- Prophet

Crop Classification, Stage Detection & Yield Estimation Models

- Random Forest
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Support Vector Machines (SVM)
- CNN-based classifiers
- U-Net / SegNet for crop segmentation
- NDVI/EVI-based feature modeling

Crop Disease & Stress Detection (Satellite & UAV Imagery)

- CNN architectures:
- ResNet, EfficientNet, DenseNet, MobileNet

- Vision Transformers (ViT, Swin Transformer)
- Object detection:

- YOLO (v5/v8)
- Faster R-CNN

- Semantic segmentation:

- U-Net, DeepLabV3+

Text, Document & OCR Intelligence

- NLP models:
- BERT, RoBERTa, DistilBERT
- Word2Vec, FastText

- OCR & document parsing:

- Tesseract OCR
- Amazon Textract
- LayoutLM

- PDF parsing & text extraction pipelines

Geospatial & Remote Sensing Tools (Strongly Preferred)

- GDAL, Rasterio
- GeoPandas
- QGIS / ArcGIS
- Google Earth Engine
- Experience with Sentinel, Landsat, Planet, MODIS data

Soft Skills

- Robust analytical and problem-solving abilities
- Excellent communication and documentation skills
- Ability to work independently and collaboratively in cross-functional teams
- Experience mentoring and training team members

📌 AI ML Senior Engineer (Uttar Pradesh)
🏢 Rmsi
📍 Uttar Pradesh