Machine Learning Engineer

Synerpeak Consultant

3 years

0 Lacs

India

Posted:6 hours ago| Platform:

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Skills Required

learning ai model development ml monitoring analytics gis data engineering airflow dask aws gcp azure docker kubernetes processing sentinel lidar analyze python numpy pytorch tensorflow qgis spark etl sql vision collaborative sustainability networks

Work Mode

Remote

Job Type

Full Time

Job Description

💻 What You’ll Do:

Machine Learning & AI Development
Model Development: Train and optimize ML models for carbon sequestration monitoring, geospatial analytics, and predictive weathering rates.
Deep Learning: Apply CNNs, transformers, and diffusion models for remote sensing and climate forecasting.
Geospatial AI: Build ML-powered GIS tools, land-use change models, and soil mineralization estimations.
Data Engineering & MLOps
Scalable ML Pipelines: Develop large-scale data pipelines for climate, soil, and geospatial datasets using Airflow, Dask, or Spark.
Cloud & Infrastructure: Deploy ML models on AWS, GCP, or Azure using Docker, Kubernetes, and CI/CD workflows.
Big Data Processing: Work with satellite, drone, and sensor data for real-time carbon tracking.
Geospatial & Climate Data Analysis
Remote Sensing: Process data from Sentinel, Landsat, MODIS, LiDAR, integrating with Google Earth Engine (GEE) and QGIS.
Geochemistry & Soil Science: Model mineral weathering, CO2 drawdown, and climate resilience impacts.
Time-Series & Climate Data: Analyze NOAA, ERA5, CMIP6 datasets for climate pattern detection.

👀 What We’re Looking For:

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
3+ years of experience in machine learning, deep learning, or AI development.
Python (NumPy, Pandas, PyTorch, TensorFlow, Scikit-learn)
Cloud ML & MLOps (AWS, GCP, Azure, Kubernetes, Docker, CI/CD)
Geospatial & Remote Sensing (GIS, Google Earth Engine, QGIS, Sentinel/Landsat)
Big Data & Pipelines (Airflow, Dask, Spark, ETL, SQL, NoSQL)
Deep Learning & Computer Vision (CNNs, Transformers, Self-Supervised Learning)
Familiarity with geospatial data, climate modeling, or environmental science is a plus.
Strong problem-solving skills and the ability to work in a collaborative team environment.

🔖 Preferred Qualifications:

Experience in climate tech, sustainability, or carbon markets.
Contributions to open-source ML or environmental science projects.
Background in graph neural networks, diffusion models, or self-supervised learning.

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