Senior Python Django Developer (3-5 yrs)

3-5 years

Key Responsibilities

• Implementing Technical Designs:

  Collaborate with architects and senior stakeholders to understand high-level designs and break them down into detailed engineering tasks. Implement system modules and ensure alignment with architectural direction.

• Cross-Functional Collaboration:

  Work closely with software developers, data scientists, and UI/UX teams to translate system requirements into working code. Clearly communicate technical concepts and implementation plans to internal teams.

• Stakeholder Support:

  Participate in discussions with product and client teams to gather requirements. Provide regular updates on development progress and raise flags early to manage expectations.

• System Development & Integration:

  Develop, integrate, and maintain components of AI/ML platforms and data-driven applications. Contribute to scalable, secure, and efficient system components based on guidance from architectural leads.

• Issue Resolution:

  Identify and debug system-level issues, including deployment and performance challenges. Proactively collaborate with DevOps and QA to ensure resolution.

• Quality Assurance & Security Compliance:

  Ensure that implementations meet coding standards, performance benchmarks, and security requirements. Perform unit and integration testing to uphold quality standards.

• Agile Execution:

  Break features into technical tasks, estimate efforts, and deliver components in sprints. Participate in sprint planning, reviews, and retrospectives with a focus on delivering value.

• Tool & Framework Proficiency:

  Use modern tools and frameworks in your daily workflow, including AI/ML libraries, backend APIs, front-end frameworks, databases, and cloud services, contributing to robust, maintainable, and scalable systems.

• Continuous Learning & Contribution:

  Keep up with evolving tech stacks and suggest optimizations or refactoring opportunities. Bring learnings from the industry into internal knowledge-sharing sessions.

• Proficiency in using AI-copilots for Coding:

  Adaptation to emerging tools and knowledge of prompt engineering to effectively use AI for day-to-day coding needs.

Technical Skills

• Hands-on experience with Python-based AI/ML development using libraries such as

  TensorFlow

  ,

  PyTorch

  ,

  scikit-learn

  , or

  Keras

  .
• Hands-on exposure to

  self-hosted or managed LLMs

  , supporting integration and fine-tuning workflows as per system needs while following architectural blueprints.

• Practical implementation of NLP/CV modules

  using tools like

  SpaCy

  ,

  NLTK

  ,

  Hugging Face Transformers

  , and

  OpenCV

  , contributing to feature extraction, preprocessing, and inference pipelines.
• Strong backend experience using

  Django

  ,

  Flask

  , or

  Node.js

  , and API development (REST or GraphQL).
• Front-end development experience with

  React

  ,

  Angular

  , or

  Vue.js

  , with a working understanding of responsive design and state management.

• Development and optimization of data storage solutions

  , using

  SQL

  (PostgreSQL, MySQL) and

  NoSQL

  (MongoDB, Cassandra), with hands-on experience configuring indexes, optimizing queries, and using caching tools like

  Redis

  and

  Memcached

  .

• Working knowledge of microservices and serverless patterns

  , participating in building modular services, integrating event-driven systems, and following best practices shared by architectural leads.

• Application of design patterns

  (e.g., Factory, Singleton, Observer) during implementation to ensure code reusability, scalability, and alignment with architectural standards.
• Exposure to big data tools like

  Apache Spark

  , and

  Kafka

  for processing datasets.
• Familiarity with

  ETL workflows and cloud data warehouse

  , using tools such as

  Airflow

  ,

  dbt

  ,

  BigQuery

  , or

  Snowflake

  .
• Understanding of

  CI/CD

  ,

  containerization

  (Docker),

  IaC

  (Terraform), and

  cloud platforms

  (AWS, GCP, or Azure).

• Implementation of cloud security guidelines

  , including setting up

  IAM roles

  , configuring

  TLS/SSL

  , and working within secure

  VPC

  setups, with support from cloud architects.
• Exposure to

  MLOps practices

  , model versioning, and deployment pipelines using

  MLflow

  ,

  FastAPI

  , or

  AWS SageMaker

  .

• Configuration and management of cloud services

  such as

  AWS EC2

  ,

  RDS

  ,

  S3

  ,

  Load Balancers

  , and

  WAF

  , supporting scalable infrastructure deployment and reliability engineering efforts.

Personal Attributes

• Proactive Execution and Communication:

  Able to take architectural direction and implement it independently with minimal rework with regular communication with stakeholders

• Collaboration:

  Comfortable working across disciplines with designers, data engineers, and QA teams.

• Responsibility:

  Owns code quality and reliability, especially in production systems.

  Problem Solver:

  Demonstrated ability to debug complex systems and contribute to solutioning.

Preferred Skills:

Key

Qualifications

• Bachelors or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
• Relevant certifications in cloud or machine learning are a plus.

Package: 6-11 LPA