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Lead Python Engineer - Flask/Django

HRABLE TECHNOLOGIES PRIVATE LIMITED
Bangalore
4 - 6 Years

Posted on: 22/10/2025

Job Description

Job Description :


Key Responsibilities :


- Lead the design, development, and deployment of Python-based applications and services.


- Define technical architecture, ensure code quality, performance, and scalability of applications.


- Develop RESTful APIs and microservices using frameworks like Flask, Django, or FastAPI.


- Drive adoption of best coding practices, automated testing, and CI/CD principles across the team.


- Conduct code reviews, provide technical mentorship, and foster an environment of learning and innovation.


- Work extensively with Apache Spark, PySpark, and related big data technologies for data transformation and analytics.


- Manage and maintain database systems (SQL, PostgreSQL, MongoDB, etc.) ensuring data integrity and performance.


- Implement serverless solutions, containerization (Docker, Kubernetes), and infrastructure automation where applicable.


- Ensure security, scalability, and high availability of cloud-based services.


- Work closely with Product Managers, Data Scientists, and DevOps teams to deliver end-to-end solutions.


- Translate business requirements into technical specifications and actionable tasks.


- Manage project timelines, sprint planning, and release cycles to ensure on-time delivery.


Mandatory Skills & Experience :


- 4-6 years of experience in software engineering with a strong focus on Python-based solutions.


- Proven experience with Python frameworks (Flask, Django, FastAPI).


- Strong knowledge of SQL and database technologies (PostgreSQL, MySQL, MongoDB).


- Solid understanding of cloud platforms (AWS, Azure, or GCP) with hands-on deployment experience.


- Familiarity with container orchestration tools (Docker, Kubernetes).


- Excellent analytical, debugging, and problem-solving abilities.


Preferred Skills :


- Exposure to data science and analytics tools such as R, Pandas, NumPy, Scikit-learn.


- Experience with data lakes, warehouses, or streaming platforms (Kafka, Kinesis).


- Familiarity with machine learning model integration within production-grade systems.

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