Python Developer

Qsix; India; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Qsix in India is seeking a hands-on Python Developer to build an in-house LLM solution and automate data processing across emails, text files, and ZIP folders. You will design RESTful APIs, manage databases, and deliver a web portal to monitor records, processing status, and audit history with robust error handling and monitoring.

The role emphasizes maintainable, scalable code and collaboration with stakeholders.

Qualifications

• Proficient in Python and OOP principles.
• Experience building in-house AI/LLM solutions.
• Ability to process data from emails, text files, and ZIPs.
• Develop RESTful APIs and web portals.
• Knowledge of SQL and database design.

Responsibilities

• Design and implement Python-based applications and automation.
• Develop in-house LLM solution without external APIs.
• Handle data extraction and processing from multiple sources.
• Create REST APIs and integrate with databases.
• Build a web portal to monitor workflow and audit history.
• Implement logging, validation, and retry mechanisms.
• Collaborate with stakeholders to translate requirements.

Skills

Python Development

OOP Concepts

Async Programming

API Development

Data Parsing

Automation

SQL & Databases

Git & Testing

Tools

FastAPI

Flask

Django

PostgreSQL

SQLAlchemy

Docker

Job description

Job Desciption

We are looking for a skilled Python Developer with strong knowledge of object-oriented programming, database management, API development, data extraction, and intelligent automation. The candidate will be responsible for developing an in-house Large Language Model (LLM) solution, processing data from multiple sources, automating email handling, and building a web-based portal to monitor and track the complete workflow.

The candidate should be capable of developing a custom AI/LLM solution without depending on external AI APIs or prebuilt AI-serving platforms such as OpenAI APIs, Azure AI APIs, or Ollama. The role requires an understanding of LLM architecture, model training or fine-tuning, data preprocessing, and local model inference.

Key Responsibilities

• Design, develop, and implement Python-based applications and automation solutions.
• Develop an in-house LLM solution using suitable open-source model architectures and frameworks, without relying on external AI APIs or Ollama.
• Understand LLM fundamentals, including tokenization, embeddings, transformer architecture, model training, fine-tuning, and inference.
• Extract, parse, validate, and process data from text files, email bodies, email attachments, and ZIP-compressed folders.
• Implement automated email processing, including reading emails, extracting relevant information, and marking emails as read or unread based on defined business rules.
• Develop RESTful APIs to process requests, exchange data, and integrate application components.
• Design and manage databases for storing extracted data, processing history, application logs, and workflow status.
• Develop a web-based portal to track processed records, email details, extraction results, processing status, failures, and audit history.
• Implement error handling, logging, data validation, and retry mechanisms to ensure reliable processing.
• Apply object-oriented programming (OOP) principles and maintain clean, reusable, scalable, and maintainable code.
• Write optimized SQL queries, design database schemas, and manage relationships, indexes, and transactions.
• Collaborate with stakeholders to understand business requirements and convert them into technical solutions.

Required Technical Skills

1. Python Development

• Strong knowledge of Python programming.
• Experience with OOP concepts, classes, objects, inheritance, polymorphism, encapsulation, and abstraction.
• Exception handling, file handling, modules, packages, and asynchronous programming.
• Experience with libraries for file processing, data extraction, and automation.

2. LLM and Machine Learning

• Understanding of LLM architecture and transformer-based models.
• Knowledge of tokenization, embeddings, attention mechanisms, and model inference.
• Ability to build, train, or fine-tune an LLM using available open-source model architectures.
• Knowledge of PyTorch or TensorFlow and relevant machine learning fundamentals.
• Ability to run and manage models locally without depending on external AI APIs or Ollama.
• Understanding of hardware requirements, GPU utilization, model quantization, and inference optimization.

3. Data Parsing and Email Automation

• Experience extracting structured information from TXT files and email bodies.
• Knowledge of processing ZIP files and handling multiple files within compressed folders.
• Experience with email protocols and APIs such as IMAP, SMTP, or Microsoft Graph, as applicable.
• Ability to manage email read/unread status and maintain email processing history.
• Knowledge of regular expressions, text normalization, and data validation.

4. API and Web Portal Development

• Experience developing REST APIs using FastAPI, Flask, or Django.
• Ability to integrate APIs with databases and background processing jobs.
• Experience developing a portal or dashboard for monitoring records, workflow status, errors, and reports.
• Knowledge of authentication, authorization, and API security.

5. Database and SQL

• Strong knowledge of SQL and relational databases such as SQL Server, PostgreSQL, or MySQL.
• Experience with joins, subqueries, aggregations, stored procedures, indexing, and query optimization.
• Knowledge of database design, transactions, data integrity, and audit logging.
• Experience using Python database connectors or an ORM such as SQLAlchemy.

6. Software Engineering Fundamentals

• Strong understanding of OOP, SOLID principles, clean code, and modular architecture.
• Knowledge of Git, debugging, unit testing, and deployment practices.
• Ability to troubleshoot issues and optimize application performance.

Preferred Skills

• Experience building local AI inference pipelines and custom NLP solutions.
• Knowledge of document classification, information extraction, and entity recognition.
• Experience with background job processing and scheduling.
• Familiarity with Docker or other deployment approaches, where applicable.
• Knowledge of the Crown job / Crown Jobs process, including its business workflow and automation requirements.

Expected Deliverables

• A locally deployable, in-house LLM solution.
• Automated extraction and processing of data from emails, text files, and ZIP folders.
• Email read/unread status management and processing automation.
• Secure APIs for data processing and retrieval.
• A database-backed portal for tracking records, processing status, errors, and audit history.
• Technical documentation, deployment instructions, and maintainable source code.

Ideal Candidate

The ideal candidate should be a hands-on developer who can independently design, develop, test, and deploy a complete solution combining Python, local LLM technology, email automation, API development, and database-driven web applications. The candidate must be comfortable building solutions in-house rather than depending on third-party AI APIs.