Project 1: AI Agent-based Legal Contract Verification System
Positions Available: 2 ML Interns
Project Overview:
This project focuses on developing an intelligent system that leverages artificial intelligence to automate the verification and analysis of legal contracts. The system will use natural language processing/Gen AI techniques to extract key clauses, identify potential risks, verify compliance with regulatory requirements, and flag anomalies that might require human attention. The AI agent will aim to reduce the time legal professionals spend on routine document review while improving accuracy and consistency.
Required Skills:
– Python programming with strong data structures and algorithms foundation
– Natural Language Processing fundamentals (text preprocessing, embeddings, transformers)
– Experience with ML frameworks (PyTorch, TensorFlow, or scikit-learn)
– Understanding of RESTful API development using FastAPI
– Familiarity with foundation models and prompt engineering
Project 2: AI Agent-based Video Platform for Financial Services Industry
Positions Available: 2 ML Interns
Project Overview:
This innovative project aims to develop an intelligent video platform specifically designed for the financial services sector. The platform will leverage AI to analyze financial video content (such as earnings calls, expert interviews, and financial news), automatically generate insights, transcribe content, summarize key points, and enable semantic search capabilities. The solution will help financial professionals quickly extract valuable information from video content to support investment decisions, market analysis, and client advisory services.
Key Responsibilities:
– Implement video processing pipelines for extracting audio and visual features
– Finetune Foundation models for the accuracy of transcription and translation.
– Build agentic workflows to perform specific analysis on Visual and audio conversations.
– Pre-fill structured forms using audio and video content.
– Create guidance for the information capture for the user of the system.
Required Skills:
– Python programming with data processing experience
– Natural Language Processing techniques and libraries
– Experience with audio processing and speech recognition
– Familiarity with ML concepts and GenAI
– Understanding of RESTful API development using FastAPI
Project 3: Agentic AI-based Services for the Financial Services Sector
Positions Available: 2 ML Interns
Project Overview:
This forward-looking project focuses on creating autonomous AI agents that can perform complex tasks across various use cases in the financial services industry. These agents will combine multiple AI capabilities (OCR, computer vision, NLP, and reasoning) to automate processes such as document processing, compliance verification, customer service, fraud detection, and personalized financial advice. The project aims to push the boundaries of what’s possible with agentic AI systems in high-value, regulated environments.
Key Responsibilities:
– Develop optical character recognition (OCR) systems for extracting information from financial documents
– Implement computer vision models for analyzing visual elements in documents and identification cards
– Create natural language processing pipelines for understanding customer queries and document content
– Design autonomous agent architectures that combine multiple AI capabilities
– Build reasoning and decision-making components that adhere to financial regulations
– Implement integration points with existing financial systems and databases
Required Skills:
– Python programming with data processing
– Computer Vision techniques and frameworks
– Optical Character Recognition
– Natural Language Processing and understanding
– Machine Learning fundamentals, Generative AI
– Knowledge of agent-based systems and architectures
– Understanding of RESTful API development using FastAPI
Project 4: Banking API Integration
Positions Available: 2 Software Development Interns
Project Overview:
As part of the Dvara team, this project focuses on integrating their financial product suite with various banking APIs. The work involves building robust, secure, and scalable connections to banking systems for account verification, payments processing, transaction monitoring, and other financial operations. The integration layer will need to handle multiple banking protocols, ensure data security, maintain high reliability, and adapt to varying API standards across different financial institutions.
Key Responsibilities:
– Develop integration modules connecting Dvara’s products with banking APIs
– Implement secure authentication mechanisms and data encryption
– Create unified data models to normalize information across different banking systems
– Build monitoring and logging systems to track API performance and connectivity
– Design fault-tolerant mechanisms for handling API failures and retries
– Implement data validation and transformation pipelines
– Assist in developing AI-powered features to enhance banking integrations
Required Skills:
– Python and/or Node.js programming
– Experience with RESTful APIs and API integration
– Understanding of HTTP/HTTPS protocols and web services
– Knowledge of the FastAPI framework
– Familiarity with authentication methods and data security
Project 5: Commodity Price Forecasting
Positions Available: 2 ML Interns
Project Overview:
This project at Dvara focuses on developing an advanced forecasting system for predicting commodity prices, with particular emphasis on agricultural products. The system will incorporate historical price data, seasonal patterns, market indicators, weather data, and other relevant factors to generate accurate price projections. These forecasts will help farmers, traders, and financial institutions make informed decisions about planting, harvesting, selling, and financing agricultural commodities.
Key Responsibilities:
– Collect and preprocess historical commodity price data from various sources
– Implement time series forecasting models using statistical and machine learning approaches
– Develop API endpoints for accessing forecasting results and historical analyses
– Create visualization tools for displaying price trends and forecast confidence intervals
– Design mechanisms for continuous model evaluation and improvement
– Implement AI-enhanced features to improve forecast accuracy
Required Skills:
– Python and/or Node.js programming
– Experience with time series analysis and forecasting techniques
– Familiarity with statistical models and machine learning for prediction
– Knowledge of data visualization libraries and techniques
– Understanding of RESTful API development using FastAPI
– Interest in agricultural economics or commodity markets (preferred but not required)
– Ability to work with large datasets and perform data cleaning
Project 6: Perennial Score Analysis
Positions Available: 1-2 ML Interns
Project Overview:
This project focuses on developing a scoring mechanism to assess coffee plantation areas using satellite imagery from the date of sowing. The analysis emphasizes identifying and analyzing the perennial crop patterns to evaluate plantation health and productivity.
Key Responsibilities:
– Process and analyze satellite imagery data over time
– Extract canopy coverage and perennial vegetation patterns
– Develop a scoring model to assess plantation areas
– Build tools to visualize spatial and temporal trends
– Support integration of results with GIS platforms
Required Skills:
– Python, with experience in geospatial data processing (e.g., GDAL, rasterio)
– Familiarity with remote sensing concepts and NDVI-based analysis
– Understanding of vegetation indices and classification techniques
– Basic data visualization and modelling skills
Drone-Based Coffee Plantation Analysis
Positions Available: 1 ML/CV Intern
Project Overview:
This project involves analysing and processing drone imagery to detect, count, and monitor the growth of coffee plants. The goal is to build models that can automate plant detection and assess growth stages using high-resolution aerial data.
Key Responsibilities:
– Process drone imagery and annotate training data
– Develop models for plant detection and segmentation
– Implement counting algorithms and growth assessment logic
– Visualize changes across time using aerial images
– Collaborate to integrate results with agronomic insights
Required Skills:
– Python, OpenCV, and deep learning frameworks (e.g., PyTorch or TensorFlow)
– Experience with image segmentation and object detection
– Basic knowledge of georeferencing and aerial image preprocessing
– Understanding of data labelling and model evaluation