Full Stack Engineer

You will contribute to the following areas of work:

Web Application Development:

- Develop, test, and maintain scalable and robust full-stack applications using React.js, Python, Restful API, Postgre

SQL.
- Build and optimize front-end user interfaces for performance and seamless user experience.
- Design and implement server-side logic and APIs, ensuring efficiency and security.

Data Analysis and Product Development:

- Scope, define, and deliver AI-based data products covering data analysis, visualization, storytelling, and data technologies
- Develop predictive models for cash flow forecasting and optimization
- Create risk assessment models for lending decisions and credit scoring
- Design and implement anomaly detection systems for fraud prevention in financial transactions

Machine Learning and AI:

- Build models, algorithms, simulations, and performance evaluation by writing highly optimized, deployable code using state-of-the-art machine learning technologies
- Apply NLP techniques for mining knowledge from public and enterprise-proprietary data (structured, unstructured, and semi-structured) to derive insights that will help downstream processes
- Develop and serve models with PyTorch / Tensor Flow (TF) for deep learning research
- Implement reinforcement learning algorithms for automated cash management strategies

Financial Technology Integration:

- Develop algorithms for real-time transaction categorization and financial pattern recognition
- Create models for personalized financial advice and product recommendations
- Implement time series analysis techniques for financial trend prediction and seasonality detection
- Design and develop APIs for integrating machine learning models with financial systems and third-party services

Data Engineering and MLOps:

- Build solutions for data discovery, acquisition, processing & cleaning, integration & storage, and interpretation
- Define and manage the process of production using machine learning models through MLOps pipelines for development, continuous integration, continuous delivery, verification & validation, and monitoring of AI/ML models
- Implement data pipelines for ingesting and processing financial data from various sources (banks, payment gateways, accounting software)

Stakeholder Collaboration and Business Impact:

- Translate business requirements into tangible solution specifications and high-quality, on-time deliverables
- Work with stakeholders to analyze & solve business problems using Machine Learning & Artificial Intelligence capabilities & support deployment on a cloud platform
- Collaborate with UX/UI teams to integrate data-driven insights into user-friendly app features
- Provide data-driven recommendations for product development and business strategy

Continuous Learning and

Innovation:

- Stay abreast of industry trends, innovations, and developments in AI/ML and work with other ML teams to pilot new advances and keep the organization future-ready
- Contribute to the development of innovative fintech products and features

Desired Skills / Expertise:

- 3-5 years of experience in full stack development
- Excellent communication skills with a desire to work in multidisciplinary teams
- Ability to explain complex technical concepts to non-technical stakeholders
- Strong experience and mathematical understanding in one or more of Natural Language Understanding, Computer Vision, Machine Learning, and Optimization
- Proven track record in effectively building and deploying ML systems using frameworks such as PyTorch, Tensor Flow, Keras, scikit-learn, etc.
- Expertise in modular, typed, and object-oriented Python programming
- Proficiency with core data science languages (such as Python, R, Scala), and familiarity & flexibility with data systems (e.g., SQL, No

SQL, knowledge graphs)

- Experience with financial data analysis, time series forecasting, and risk modeling
- Knowledge of financial regulations and compliance requirements in the fintech industry
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes)
- Understanding of blockchain technology and its applications in fintech

- Experience with real-time data processing and streaming analytics

Educational

Qualifications:

- Bachelor's / Master's / Ph.D. in Computer Science, Data Science, Financial Engineering, or related technical fields from tier 1 or tier 2 college
- Relevant certifications in data science, machine learning, or financial technology are a plus