Python & Full-Stack Engineer

Randstad India

Job Location:

Chennai - India

Monthly Salary: Not Disclosed

Posted on: 3 days ago

Vacancies: 1 Vacancy

Job Summary

Job Title: Python & Full-Stack Engineer (Data & Automation)

Role Overview: We are looking for an experienced production-focused Python & Full-Stack Engineer with 3 years of professional experience to join our core engineering team. This role is designed for a proven builder-someone who has spent the last few years designing deploying and maintaining reliable backend systems interactive data interfaces and automation pipelines in production environments. You will take ownership of our data visualization infrastructure full-stack application layers automated reporting engines and industrial telemetry pipelines. While experience with Artificial Intelligence and Machine Learning frameworks is a strong asset your core engineering ownership will drive our primary data processing reporting and systems integration architecture.

Technical Requirements (Must-Haves)

Professional Experience: 3 years of commercial software development experience in a production environment.
Data Analysis Stack: Deep production-level expertise in Python specifically utilizing Pandas NumPy and SciPy for heavy data manipulation and computation.
Visualization & Dashboards: Proven track record of building advanced interactive visuals using Plotly deploying production-grade web applications with Plotly Dash and generating clean static plots via Matplotlib.
Full-Stack Development: Solid commercial experience building complete software solutions from the user interface down to the backend APIs and database schemas.
File I/O & Programmatic Reporting: Hands-on experience parsing complex structured files (CSV XLSX) and programmatically generating documents using Python PDF generation packages (such as ReportLab FPDF or WeasyPrint).
Database Integration: Strong proficiency in relational databases and SQL including backend integration via Python SQL packages (such as SQLAlchemy psycopg2 or pyodbc).
Windows & System Automation: Practical experience developing and deploying Windows Services managing automated workflows via the Windows Task Scheduler and implementing enterprise-grade Logging strategies for system audits.
Core Environment: Mastery of Git version control Linux/Windows environments and shell scripting.

The Edge (Preferred Qualifications / Good to Have)

AI/ML Exposure: Experience or strong interest in integrating Machine Learning frameworks (PyTorch or TensorFlow) handling Vector Databases or implementing Agentic/RAG workflows.
Hardware & Systems Awareness: Practical understanding of low-level optimization (C or C) or hands-on deployment with edge computing modules like NVIDIA Jetson or Raspberry Pi.
Domain Interest: Experience or curiosity regarding industrial automation mining systems heavy machinery telemetry or complex mechanical data pipelines.

Soft Skills

Analytical Rigor: You approach system bugs data anomalies and edge cases systematically engineering long-term stability rather than quick fixes.
Autonomy & Self-Direction: You are comfortable taking high-level functional requirements and translating them into robust architectural code with minimal supervision.
Technical Communication: Ability to collaborate effectively with cross-functional engineering teams and clearly explain data trends or system limits to non-technical stakeholders.

Key Responsibilities

Dashboard & Full-Stack Ownership: Architect optimize and maintain end-to-end full-stack applications and highly interactive responsive data dashboards for live telemetry and data analysis.
Production Data Engineering: Design efficient data processing pipelines to clean transform and aggregate large raw industrial datasets ensuring high-performance memory management.
Automated Enterprise Reporting: Build and maintain robust server-side document generation engines that programmatically compile complex technical data into highly formatted print-ready document exports.
System Automation & OS Integration: Develop and deploy reliable background processing architectures custom system services and scheduled tasks designed for 24/7 reliability in industrial enterprise environments.
Database & Observability: Optimize structured relational database layers write high-performance SQL queries and implement rigorous structured logging practices to ensure absolute system traceability and rapid debugging.

Job Title: Python & Full-Stack Engineer (Data & Automation) Role Overview: We are looking for an experienced production-focused Python & Full-Stack Engineer with 3 years of professional experience to join our core engineering team. This role is designed for a proven builder-someone who has spent ...

Job Title: Python & Full-Stack Engineer (Data & Automation)

Technical Requirements (Must-Haves)

Professional Experience: 3 years of commercial software development experience in a production environment.
Data Analysis Stack: Deep production-level expertise in Python specifically utilizing Pandas NumPy and SciPy for heavy data manipulation and computation.
Visualization & Dashboards: Proven track record of building advanced interactive visuals using Plotly deploying production-grade web applications with Plotly Dash and generating clean static plots via Matplotlib.
Full-Stack Development: Solid commercial experience building complete software solutions from the user interface down to the backend APIs and database schemas.
File I/O & Programmatic Reporting: Hands-on experience parsing complex structured files (CSV XLSX) and programmatically generating documents using Python PDF generation packages (such as ReportLab FPDF or WeasyPrint).
Database Integration: Strong proficiency in relational databases and SQL including backend integration via Python SQL packages (such as SQLAlchemy psycopg2 or pyodbc).
Windows & System Automation: Practical experience developing and deploying Windows Services managing automated workflows via the Windows Task Scheduler and implementing enterprise-grade Logging strategies for system audits.
Core Environment: Mastery of Git version control Linux/Windows environments and shell scripting.

The Edge (Preferred Qualifications / Good to Have)

AI/ML Exposure: Experience or strong interest in integrating Machine Learning frameworks (PyTorch or TensorFlow) handling Vector Databases or implementing Agentic/RAG workflows.
Hardware & Systems Awareness: Practical understanding of low-level optimization (C or C) or hands-on deployment with edge computing modules like NVIDIA Jetson or Raspberry Pi.
Domain Interest: Experience or curiosity regarding industrial automation mining systems heavy machinery telemetry or complex mechanical data pipelines.

Soft Skills

Analytical Rigor: You approach system bugs data anomalies and edge cases systematically engineering long-term stability rather than quick fixes.
Autonomy & Self-Direction: You are comfortable taking high-level functional requirements and translating them into robust architectural code with minimal supervision.
Technical Communication: Ability to collaborate effectively with cross-functional engineering teams and clearly explain data trends or system limits to non-technical stakeholders.

Key Responsibilities

Dashboard & Full-Stack Ownership: Architect optimize and maintain end-to-end full-stack applications and highly interactive responsive data dashboards for live telemetry and data analysis.
Production Data Engineering: Design efficient data processing pipelines to clean transform and aggregate large raw industrial datasets ensuring high-performance memory management.
Automated Enterprise Reporting: Build and maintain robust server-side document generation engines that programmatically compile complex technical data into highly formatted print-ready document exports.
System Automation & OS Integration: Develop and deploy reliable background processing architectures custom system services and scheduled tasks designed for 24/7 reliability in industrial enterprise environments.
Database & Observability: Optimize structured relational database layers write high-performance SQL queries and implement rigorous structured logging practices to ensure absolute system traceability and rapid debugging.