Software Engineer at CUBE
CUBE is a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. Operating across 19 countries with over 700 employees, CUBE delivers intuitive SaaS solutions powered by AI to simplify complex compliance requirements for top financial institutions globally.
Role Overview
About CUBE
CUBE is a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. Operating across 19 countries with over 700 employees, CUBE delivers intuitive SaaS solutions powered by AI to simplify complex compliance requirements for top financial institutions globally.
Role Summary
As a Software Engineer, you will design, develop, and maintain scalable AI-powered data ingestion and processing platforms using Python. You will build intelligent data pipelines, web crawling solutions, and AI/ML services that process structured and unstructured data while ensuring high scalability, performance, and reliability.
Eligibility Criteria & Qualifications
- 2-3 years of professional Python development experience with frameworks like FastAPI or Django.
- Bachelor's or Master's degree in Computer Science, Information Technology, or related engineering fields.
- Proven experience in AI/ML, Generative AI, or LLM application development using LangChain or LlamaIndex.
- Hands-on experience with web scraping frameworks (Scrapy, Playwright) and document parsing libraries (BeautifulSoup, PyMuPDF).
Key Responsibilities
Model Development & Experimentation
- •Design and develop web crawling, data ingestion, and AI-powered processing solutions using Python.
- •Implement AI/ML models or integrate LLMs for document parsing, classification, summarization, and information extraction.
Data Pipelines & Feature Engineering
- •Build scalable data pipelines for processing HTML, PDF, and other structured/unstructured data sources.
- •Build monitoring, logging, and observability for AI pipelines and backend microservices.
Evaluation, Deployment & Optimization
- •Develop REST APIs to support AI/ML services and background processing workflows.
Required Skills & Technologies
How to Apply
Submit your application directly through CUBE's verified careers portal.
Review the published role requirements, degree qualifications, and eligible graduation batches above.
Ensure your resume highlights relevant projects and contains working GitHub repository links.
Click the official application button below to open CUBE's direct careers page.
Complete and submit your application directly on the employer's portal.
You will be directed to CUBE's official careers portal. Career Insights does not process applications or charge any candidate fees.
The specifications above are verified official details published by CUBE. Candidates should verify all final terms, deadlines, and eligibility directly on CUBE's official careers portal before submitting an application.
Recommended Preparation
Independent editorial guidance based on the published role information.
What to Prepare
Evaluate your machine learning background against published criteria and modeling preparation focus areas.
B.E / B.Tech / MCA / B.Sc
Hiring evaluations prioritize demonstrable problem-solving, domain competence, and practical project evidence over specific degree pedigree.
Applied machine learning, statistical modeling, and Python libraries.
Revise ML evaluation metrics (Precision, Recall, F1, ROC-AUC), feature engineering, loss functions, and model deployment via REST endpoints (FastAPI).
Model development, dataset preprocessing, and experimentation.
Understand dataset splits, regularization techniques to prevent overfitting, vector embeddings, and modular code hygiene in ML pipelines.
Familiarity with databases, schema modeling, and structured query writing.
Be prepared to explain relational normalization (1NF-3NF), indexing trade-offs, foreign key constraints, and transactional consistency.
Familiarity with Git branching, pull requests, and collaborative repository management.
Review Git branch workflows, pull request code reviews, and maintain clean commit history with descriptive messages.
Onsite (Bangalore) operational structure.
Highlight proactive written communication, technical documentation discipline, and the ability to troubleshoot errors systematically before escalating.
Technical Topics
Depending on the hiring team’s evaluation style, technical assessments for this role typically cover these core areas:
Topic 1: Python & Machine Learning Fundamentals
- Python data science libraries (NumPy, Pandas, PyTorch / TensorFlow)
- Supervised and unsupervised ML algorithms, loss functions, and optimizers (SGD, Adam)
- Feature normalization, embeddings, train/validation split discipline, and preventing data leakage
Topic 2: NLP, LLM APIs & Transformer Concepts
- Tokenization, embeddings, attention mechanisms, and Transformer architectures
- Prompt engineering, structured output extraction, and LLM API integration
- Retrieval-Augmented Generation (RAG) and semantic search workflows
Topic 3: Document Understanding & Computer Vision
- Document layout analysis, optical character recognition (OCR) pipelines, and bounding-box validation
- Table extraction, key-value pair parsing, and multimodal vision-language concepts
- Handling image noise, scan distortions, and irregular document structures
Topic 4: Evaluation Methodology, Metrics & Error Diagnostics
- Precision, Recall, F1-Score, ROUGE, BLEU, and exact match metrics
- Curating rigorous test sets and designing stratified validation splits
- Error analysis: categorizing model failure modes, confusion matrices, and verifiable AI
Topic 5: Experimentation, Research Documentation & Reproducibility
- Systematic experiment tracking (MLflow, Weights & Biases) and ablation study design
- Documenting research hypotheses, benchmarks, and reproducible pipelines
- Communicating model confidence thresholds, error taxonomy, and verifiable AI considerations
* Note: These are independent Career Insights editorial recommendations to help candidates structure their study plan. They do not represent guaranteed interview stages or official hiring mandates from CUBE.
Practical Project
Demonstrate the machine-learning workflow by building a small project that covers data preparation, modeling, evaluation, and reproducible results.
Document Intelligence & Agentic Table Extraction Pipeline
A reproducible document intelligence pipeline in Python that extracts structured tabular data from complex, multi-page PDF documents or scanned invoices using layout-aware models or LLM-assisted verification, complete with automated benchmark scoring.
Python, PyTorch / Transformers, Document AI & OCR, Model Evaluation & Error Diagnostics, Test-Set Curation, Reproducible Research Documentation.
Clean repository with environment setup (requirements.txt), sample evaluation dataset, benchmark scripts with precision/recall scoring, error analysis notebooks, and clear architecture documentation.
How you designed your evaluation test set, failure mode taxonomy (how the model handles rotated scans or dense tables), and the trade-offs between heuristic rules vs deep learning models.
Career Insights recommendation: This project is an independent editorial recommendation designed to help candidates build verifiable portfolio evidence. It is not required or sponsored by CUBE.
Before You Apply
Verify your model notebooks, evaluation summaries, and application materials before applying.
Ensure your resume clearly highlights hands-on projects involving Python, FastAPI / Django, Generative AI & LLM Integration. Include verified, publicly accessible GitHub repository links with clean README files.
Confirm that your graduation year (Not specified), educational background, and location match the employer's published requirements before applying.
Have 1–2 key projects prepared for technical discussion. Be ready to explain your database schema choices, API contracts, error boundaries, and design trade-offs.
Submit your application directly through CUBE's official careers portal using the verified apply link below. Career Insights does not process applications or charge any candidate fees.
Career Growth Context
Career Insights editorial perspective on typical milestones and research contribution cycles in applied ML internships.
Research Orientation & Baseline Benchmarking
Familiarizing oneself with the team’s research tools, dataset pipelines, and core model baselines under technical guidance.
Hands-on Experimentation & Error Diagnostics
Conducting exploratory modeling experiments, benchmarking against baselines, and performing rigorous error analysis on complex inputs.
Validation, Reproducibility & Research Documentation
Documenting experimental methodology, optimizing inference trade-offs, and validating agentic or document intelligence workflows.
Performance-Based Conversion Opportunity
Demonstrating consistent research rigor, problem-solving, and technical ownership. As noted in available employer postings, full-time conversion represents an opportunity evaluated based on individual performance and organizational needs rather than a guaranteed progression.
This page provides verified job details sourced from CUBE's recruitment announcement, alongside independent preparation roadmaps authored by the Career Insights editorial team. Career Insights is not affiliated with CUBE and does not collect candidate data or charge application fees.
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