Location

Warangal, Telangana (Hybrid/Remote)

Duration

2 Months

Mode

Onsite

Stipend

INR 5000 per month

Responsibilities

• Develop autonomous AI agents for continuous model improvement.

• Design automated machine learning (AutoML) pipelines.

• Build data validation and quality assessment workflows.

• Implement active learning and human-in-the-loop systems.

• Automate model retraining using new customer datasets.

• Monitor model performance and detect model drift.

• Integrate MLflow or similar tools for experiment tracking.

• Build scalable deployment pipelines on Google Cloud.

• Collaborate with computer vision and backend teams.

Required Skills

• Python

• PyTorch or TensorFlow

• Machine Learning

• Git

• Docker

• REST APIs

Preferred Skills

• LangGraph

• CrewAI

• AutoGen

• MLflow

• Kubernetes

• Google Cloud

• CI/CD

What You'll Learn

• Production AI systems

• Autonomous AI agents

• Continuous Learning

• MLOps

• Cloud deployment

• Large-scale AI engineering

Location
Warangal, Telangana (Hybrid/Remote)
Duration
2 Months

Mode

Onsite

Responsibilities

• • Build RAG pipelines using agricultural datasets.

  • Develop multilingual AI assistants.

  • Optimise prompt engineering and retrieval quality.

  • Integrate vector databases.

  • Build function-calling AI agents.

  • Develop voice-enabled chatbot features.

  • Evaluate LLM responses for accuracy and safety.

  • Collaborate with agronomists and AI engineers.

Required Skills

• • Python

  • NLP fundamentals

  • Transformers

  • Git

Preferred Skills

• LangChain

• LlamaIndex

• Hugging Face

• ChromaDB

• FAISS

• Pinecone

• Gemini

• Gemma

• Qwen

You'll Gain Experience In

• Production LLMs

• RAG Systems

• AI Agents

• Voice AI

• Prompt Engineering

• Agricultural AI

Location

SRiX (SR Innovation Exchange), Warangal, Telangana

Mode

Onsite

Duration

2 Months

Stipend

₹5,000 per month

Responsibilities

• Analyze multispectral and hyperspectral satellite imagery for agricultural applications.

• Compute and visualize vegetation indices such as NDVI, EVI, SAVI, NDWI, NDMI, GCI, MSAVI, and other remote sensing products.

• Develop geospatial data processing workflows using Google Earth Engine and GIS tools.

• Integrate satellite datasets from platforms such as Sentinel, Landsat, MODIS, and other Earth observation missions.

• Build interactive dashboards and web-based tools for satellite data visualization.

• Support AI model development using remote sensing and geospatial datasets.

• Collaborate with AI engineers to integrate satellite-derived insights into precision agriculture solutions.

Required Skills

• Fundamentals of Remote Sensing and GIS

• Python

• QGIS or ArcGIS

• Google Earth Engine (GEE)

• Basic image processing and geospatial data analysis

Preferred Skills

• GDAL, Rasterio, GeoPandas

• Sentinel Hub APIs

• Google Cloud Platform

• JavaScript (for GEE)

• OpenCV

• Machine Learning for Remote Sensing

What You'll Learn

• Satellite data processing for precision agriculture

• Google Earth Engine and cloud-based geospatial analysis

• AI applications in remote sensing

• Vegetation health monitoring and crop analytics

• End-to-end deployment of geospatial applications

• Industry best practices in geospatial AI and cloud technologies