Eligibility
Fresher
Recruitment Process
30 Dec 25, 08:44 PM IST–> 06 Jan 26, 08:44 PM IST
Introduction
Carnival Capital, an Indian and global financial markets trading system, is hiring a Machine Learning Engineer. This role is not just about data analysis; it involves actively participating in capital allocation and trading strategies. This article provides complete details about Carnival Capital and the role of the Machine Learning Engineer, including background, job responsibilities, required timeline, eligibility, and how to apply.
About Karanwal Capital
Capital is a technology-first proprietary trading firm and family office based in Houston, Texas, USA. The firm utilizes data science, machine learning, and algorithmic execution to identify profitable opportunities in the financial markets.Unlike traditional investment firms, Karanwal Capital builds its own in-house technology to power its trading operations. Engineers play a key role in developing the “brains” behind its automated decision-making systems. The firm actively trades in Indian and global markets using advanced predictive models and quantitative strategies.
Machine Learning Engineer Role Overview
Karanwal Capital is hiring a Machine Learning Engineer to work directly with the founder to build predictive models for financial markets.This role offers you direct ownership of real-world trading systems where your machine learning models will impact actual capital deployment. You won’t be limited to just experimentation – your work will be deployed in production and real-time market environments.This position is ideal for engineers who enjoy solving complex problems using data, mathematics, and programming, and who want to see an immediate impact from their work.
Key Responsibilities
model Development you will design, train, and improve machine learning models to predict asset price movements, trends, and volatility. The models will include:
- Time series forecasting models
- LSTM neural Networks
- Regression prediction Systems
Your models will be used to guide real-world trading decisions.
Data Pipeline Development
you will build automated pipelines for the following:
- Scraping market data, including tick and OHLCV Data
- collecting alternative data such as news and Sentiment
- efficiently cleaning, normalizing, and storing large Datasets
- ensuring that models always have accurate and up-to-date inputs.
Strategy Backtesting
You will simulate and measure trading strategies on historical market data:
- Profitability
- risk Level
- consistency across different market Conditions this process helps validate the models before applying them directly.
Optimization and Refinement
you will optimize the Python code for real-time processing during market hours. This includes:
- Improving speed and Efficiency
- ensuring system Reliability
- monitoring performance and Errors
your systems must be robust enough to operate continuously in a live trading environment.
Required Skills and Qualifications
key Programming Skills
- strong proficiency in Python
- experience with Pandas, NumPy, and Scikit Learn
Machine Learning and Ai
- experience with PyTorch, TensorFlow, or Keras
- Ability to train and tune predictive models
Financial Knowledge
- basic understanding of the stock market, derivatives, or cryptocurrencies is desirable but not essential.
- A willingness to quickly learn financial concepts is necessary.
Mathematics
- A strong foundation in statistics and probability.
- An understanding of risk, variance, and distribution is beneficial.
Recruitment Timeline and Key Dates
Karanwal Capital moves quickly and follows a rigorous recruitment schedule:
Application Deadline:January 12, 2026
Selection Completion Date: January 10, 2026
Offer Letter Issued Date: January 22, 2026
Preference will be given to candidates who can join immediately or within 15 days.
Who should apply?
You should apply if you meet these criteria:
- You have strong Python and ML Skills
- you enjoy working with financial Data
- you want to build real-world trading Systems
- you are comfortable working in a fast-paced, startup-like Environment
- you can join soon or have a short notice Period
both fresh graduates with strong technical skills and experienced engineers with a trading or ML background are encouraged to apply.
Why join Karanwal Capital?
- Direct collaboration with the Founder
- high ownership and Responsibility
- experience in global Markets
- real influence on business Decisions
- opportunity to build cutting-edge financial Technology
this role offers an excellent blend of engineering, finance, and entrepreneurship.
Frequently Asked Questions
1. Is this a remote or on-site position?
This position is based in Houston, but remote or hybrid flexibility may be available based on discussions with the team.
2. Is financial experience required?
No. Knowledge of finance is desirable but not required. Strong ML and Python skills are essential.
3. Will I work on real trading systems?
Yes. Your models will directly impact live trading decisions.
4. What kind of data will I work with?
You will work with market data such as OHLCV and tick data, as well as alternative data such as news and sentiment data.
5. Which ML models are primarily used?
Time series forecasting, LSTM networks, and regression models are commonly used.
6. Is the hiring process fast?
Yes. The company follows a strict and fast hiring timeline, with selections to be completed by January 10th.
7. Who will receive priority in the selection process?
Priority will be given to candidates who can join immediately or within 15 days.
Conclusion
The Machine Learning Engineer position at Cornwall Capital offers a unique opportunity to work at the intersection of finance, data science, and real-time decision-making. If you are passionate about building intelligent trading systems that directly impact financial outcomes, this role is the perfect platform for you.
With a fast-paced hiring process, a strong technical focus, and direct experience in real-world capital applications, this position is ideal for engineers who want to build a meaningful career in quantitative finance and machine learning.