Computer Vision Scientist

Designation – Computer Vision Scientist (Geospatial Analysis)

Location - Bangalore

Position - Full-Time (No Hybrid)

Salary – Competitive

Company QueNext

About Us

AI ML Startup set up in 2015 which is in the domain of power, banking, agriculture sector etc. and working directly with Karnataka Govt to offer insights and decisions.

Company website: -

About Us

AI ML Startup set up in 2015 which is in the domain of power, banking, agriculture sector etc. and working directly with Karnataka Govt to offer insights and decisions.

You will be part of the team that is delivering large transformational projects to

energy utilities, banks and government bodies utilizing our in-house patented AI

driven products.

You will be working on cutting edge geospatial technologies with a steep learning

curve and working along-side the founders from Indian Statistical Institute (ISI) and

INSEAD and a team of data scientists and programmers in a collaborative and

innovative environment.

You should have a strong understanding and interest in remote sensing and coding.

A superior academic record at a leading university in Computer Science, Data

Science and Technology, Geoinformatics, Mathematics, Statistics, or a related field

or equivalent work experience is preferable.

Please send in your applications to Aren at If possible, Please

include links to projects developed by you.

Job Description:

We are seeking a highly skilled and innovative Computer Vision Scientist to join us

team.

The ideal candidate will have expertise in geographic information systems (GIS),

hands-on experience with Apache Kafka, TensorFlow, and machine learning (ML)

pipelines, and a strong background in computer vision.

You will bring your familiarity and experience with satellite and aerial imagery,

geospatial APIs and libraries, knowledge of containerization and orchestration tools

(e.g., Docker, Kubernetes) and Understanding of Big Data frameworks (e.g., Spark,

Hadoop) in geospatial contexts.

This role is ideal for someone passionate about developing advanced geospatial

applications, integrating cutting-edge technologies, and solving complex spatial data

challenges.

Key Responsibilities:

GIS Development:

 Design, develop, and implement GIS-based applications and services.

 Create, manipulate, and analyze geospatial data

 Integrate geospatial data into larger software systems

Machine Learning Pipelines:

 Build, optimize, and deploy ML pipelines for geospatial and computer

vision tasks

 Leverage TensorFlow to create models for spatial analysis, object

detection, and image classification

 Implement ML workflows from data ingestion to deployment and

monitoring

Kafka Integration:

 Develop real-time data streaming and processing workflows using

Apache Kafka

 Design event-driven systems for geospatial and computer vision

applications

 Ensure scalability, reliability, and efficiency in Kafka-based pipelines

Computer Vision Applications:

 Apply computer vision techniques to geospatial data, satellite imagery,

and aerial photography

 Develop and deploy models for tasks like feature extraction, land-use

classification, and object recognition

 Stay updated on advancements in CV to enhance project outcomes

Collaboration and Documentation:

 Collaborate with cross-functional teams, including data scientists,

software engineers, and GIS analysts

 Document workflows, processes, and technical details for easy

replication and scalability

 Provide technical support and troubleshooting for GIS and ML-related

challenges

Technical Skills:

 Proficiency in GIS tools

 Strong expertise in Apache Kafka for real-time data streaming.

 Experience with TensorFlow, Keras, or PyTorch for ML model development

 Knowledge of machine learning pipelines and tools (e.g., Kubeflow, Airflow).

 Hands-on experience with computer vision techniques and libraries (e.g.,

OpenCV, TensorFlow Object Detection API).

 Strong programming skills in Python, Java, or C++.

 Familiarity with cloud platforms (e.g., AWS, Azure, GCP) for ML and GIS

deployment.

 Knowledge of geospatial data formats (e.g., GeoJSON, Shapefiles, Raster).

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