AI Research Intern (LLMs, Agentic AI & Wealth Intelligence)
Product: Wealthh.in
Location: Mumbai (Hybrid/Remote)
Duration: 6 Months
Commitment: Minimum 25 Hours Per Week
About Wealthh.in
Wealthh.in is building an AI-powered wealth management platform that combines investment research, portfolio analytics, client engagement, and financial intelligence.
As part of this vision, we are building a suite of AI agents for investment research, portfolio review, fund analysis, market intelligence, and client engagement.
We are looking for intellectually curious individuals with strong mathematical and AI foundations who want to work on the next generation of AI systems.
This is a research-oriented role rather than a traditional software development role.
Internship Structure
Phase 1 – Training & Evaluation (Months 1–3)
Unpaid training period focused on:
- Foundations of modern AI
- Large Language Models
- Agentic AI
- AI Evaluations
- Retrieval-Augmented Generation
- Financial Research Systems
- Wealth Management Domain Knowledge
Phase 2 – Paid Internship (Months 4–6)
Candidates demonstrating strong performance, commitment, and contribution will be offered a paid internship.
What You Will Work On Agentic AI Research
Study and evaluate:
- Multi-Agent Systems
- Tool Calling Architectures
- MCP Servers
- Agent Memory Systems
- Planning and Reasoning Frameworks
- AI Workflows for Financial Services
You will help design and evaluate AI agents such as:
- Portfolio Review Agent
- Research Agent
- Fund Ranking Agent
- Market Intelligence Agent
- Client Engagement Agent
AI Evaluation & Benchmarking
A major focus of this role is understanding how to measure AI performance.
Responsibilities include:
- Creating evaluation datasets
- Designing benchmarks
- Measuring hallucinations
- Evaluating reasoning quality
- Testing agent reliability
- Comparing models and prompting strategies
Tools may include:
- DeepEval
- Ragas
- LangSmith
- OpenEvals
- Promptfoo
Fine-Tuning & Model Analysis
Responsibilities include:
- Understanding transformer architectures
- Design Agnetic workflow
- Dataset creation and curation
- Fine-tuning open-source models
- Evaluating model performance
- Comparing foundation models
- Studying emerging research papers such self-improvement, AI safety
Wealth Intelligence Research
You will help build AI systems that understand:
- Mutual Funds
- Portfolio Construction
- Fund Research
- Market Trends
- Investment Behaviour
- Client Engagement
Required Background
We are less concerned about software development experience and more interested in intellectual ability and AI understanding.
Required
Strong understanding of:
- Linear Algebra
- Probability & Statistics
- Calculus
- Optimization Concepts
- Machine Learning Fundamentals
Knowledge of:
- Neural Networks
- Transformers
- Large Language Models
- Embeddings
- Attention Mechanisms
- Reinforcement Learning (basic understanding)
Preferred Backgrounds
Students or graduates from:
- Mathematics
- Physics
- Statistics
- Computer Science
- Artificial Intelligence
- Engineering
Candidates who have:
- Read AI research papers
- Built AI projects
- Experimented with open-source models
- Participated in research projects
What We Value
We are looking for people who:
- Enjoy learning difficult concepts
- Can read and understand research papers
- Think critically
- Ask good questions
- Have intellectual curiosity
- Are excited about the future of AI
Programming ability is useful, but this is not primarily a software development role.
Selection Process
- Resume Review
- AI & Mathematics Assessment
- Research Discussion
- Internship Offer
What You Will Learn
- Agentic AI Systems
- Multi-Agent Architectures
- AI Evaluations
- Fine-Tuning
- LLM Research
- WealthTech Applications
- Financial Intelligence Systems
This role is ideal for candidates considering careers in AI Research, Quantitative Research, Machine Learning, or Applied AI.
Pay: ₹5,000.00 per hour
Benefits:
Application Question(s):
- Provide a short paragraph along with GitHub: fine-tuning work undertaken & or AI agent evaluation.
Work Location: Remote
