Computer Vision & ML Engineer | Remote from Mumbai | USD Salary

HR Ways; Remote; INR 6,75,000 - 9,00,000; Full time

Job summary

An AI-based agriculture technology firm is seeking a Senior Computer Vision & ML Engineer to lead the development of image analysis models remotely from Mumbai. The role emphasizes model development for agricultural applications, requiring expertise in Python and various ML frameworks. Candidates should have experience in AI/ML, Computer Vision, or Geospatial Analytics, ideally within an agricultural context. This position includes collaborative research opportunities and the design of innovative machine learning solutions.

Qualifications

• 3-6 years of experience in AI/ML, Computer Vision, or Geospatial Analytics.
• Strong knowledge of object detection and segmentation frameworks.
• Hands-on with containerization and API deployment.

Responsibilities

• Develop next-generation models for agricultural imagery analysis.
• Design and train object detection, segmentation, and classification models.
• Collaborate on research and publish internal benchmark reports.

Skills

Python

PyTorch

OpenCV

TensorRT

NumPy

Pandas

Object detection frameworks (YOLOv8/v11)

Geospatial data tools (GDAL, GeoPandas)

Docker

FastAPI

Tools

GDAL

GeoPandas

rasterio

shapely

Job description

Computer Vision & ML Engineer | Remote from Mumbai | USD Salary

About the job

Hiring Computer Vision & ML Engineer - Remote from Mumbai India

Client Company Introduction:
An AI-based agriculture technology firm headquartered in Canada.

Position Overview

We’re seeking a Senior Computer Vision & ML Engineer to lead the development of next‑generation models for agricultural imagery analysis. You’ll own the end‑to‑end lifecycle from dataset design and deep learning model development to scalable inference and autonomous agent integration. This is a high‑impact role where research meets production, blending computer vision, MLOps, and agricultural domain knowledge into intelligent, deployable solutions.

Design and train object detection, segmentation, and classification models for aerial drone imagery (YOLO, GRIT, Mask R‑CNN, GNNs).

Build models for crop health assessment, weed detection, off‑type/volunteer identification, and yield estimation using multispectral datasets.

Apply spectral normalization, tiling, and augmentation for diverse agricultural conditions.

Geospatial Data Processing

Build and optimize orthomosaic pipelines using GDAL, OpenCV, and rasterio.

Implement coordinate alignment, geotagging, and crop‑level spatial statistics.

Integrate drone imagery with field boundaries, weather, and soil datasets.

AI Agents & Automation

Develop and orchestrate multi‑agent systems for agricultural decision support (e.g., Crop Doctor, Spray Planner, Compliance Checker).

Integrate LLMs and vector databases for contextual crop advisory and autonomous intervention planning.

Collaborate with product and data teams to automate inference and generate actionable insights.

Containerize and deploy models using Docker, FastAPI, and cloud services (AWS/GCP/Azure).

Implement inference APIs, model versioning, and automated retraining pipelines.

Ensure scalability, low‑latency inference, and cost optimization across environments.

Research & Reporting

Publish internal performance benchmarks and model evaluation reports.

Collaborate on research papers and whitepapers in agri‑AI, UAV, and geospatial analytics.

Support visualization dashboards (e.g., orthomosaic overlays, density maps, and predictive analytics).

Required Skills & Expertise

• 3-6 years of experience in AI/ML, Computer Vision, or Geospatial Analytics, ideally in agriculture, environmental science, or remote sensing.
• Expertise in Python, PyTorch, OpenCV, TensorRT, and NumPy/Pandas.
• Strong knowledge of object detection and segmentation frameworks (YOLOv8/v11, Faster R‑CNN, GRIT).
• Experience with geospatial data tools (GDAL, GeoPandas, rasterio, shapely).
• Hands‑on with containerization and API deployment (Docker, FastAPI).
• Familiarity with LLMs, LangChain, and vector databases for retrieval‑augmented systems.
• Strong understanding of drone data pipelines from image ingestion to inference output.
• Excellent documentation, collaboration, and model validation skills.

Preferred Qualifications

• Background in agriculture technology (AgTech), remote sensing, or environmental AI applications.
• Experience with Graph Neural Networks (GNNs) for spatial reasoning.
• Publications or hackathon achievements in AI/ML or UAV imagery.
• Prior experience with automated reporting dashboards (e.g., Streamlit, Power BI, or QGIS plugins).
• Exposure to LangGraph, RAG pipelines, or multi‑agent orchestration frameworks.

Other Details

• Work Mode: Remote from Mumbai, India
• Experience: 3-6 Years in ML & Computer Vision
• Working Timings: 9am-6pm