Python Backend Developer | 3+ YOE

Company Description

Axionix Technologies is a global leader in providing digital transformation solutions, working closely with top technology companies to deliver impactful results. Guided by innovation, agility, and a commitment to purpose, Axionix partners with clients from initial design to final operations. With expertise across multiple domains, the company specializes in embedding cutting-edge technology into its clients workflows. Axionix also helps organizations build quality tech teams and outsource digital projects to drive growth.

About the Role:

We are looking for a Python Backend Developer with strong experience in time series analysis, forecasting, and data-driven backend systems. You will design and build scalable backend services that power forecasting pipelines, analytics platforms, and intelligent decision systems used across the business.

Key Responsibilities:

• Design, build, and maintain robust backend services using Python.
• Develop APIs and microservices to serve time series forecasts and analytics results.
• Build and productionize forecasting pipelines for large-scale time series data.
• Implement data ingestion, preprocessing, feature engineering, and model serving workflows.
• Integrate forecasting models into production systems with monitoring and retraining pipelines.
• Optimize systems for performance, scalability, and reliability.
• Work closely with data scientists, ML engineers, and product teams to deploy forecasting solutions.
• Ensure high code quality, testing coverage, and documentation.

Required Skills:

Core Python:

• Strong Python programming experience.
• Experience with backend frameworks such as FastAPI / Flask / Django.
• REST API design and microservices architecture.
• Solid understanding of asynchronous programming and performance optimization.
• Experience with SQL and NoSQL databases (PostgreSQL, MongoDB, Redis, etc.).

Time Series and Forecasting

• Hands-on experience with time series modeling and forecasting techniques.
• Experience with libraries such as:
• statsmodels
• Prophet
• scikit-learn
• pandas / numpy
• Understanding of ARIMA/SARIMA, exponential smoothing, regression-based forecasting, or ML-based forecasting.
• Experience handling seasonality, trend decomposition, anomaly detection.

Data Systems:

• Experience working with large time series datasets.
• Knowledge of data pipelines, ETL, and batch/stream processing.
• Familiarity with message queues (Kafka/RabbitMQ) is a plus.
• Experience with Docker and cloud platforms (AWS/GCP/Azure).

Good to Have:

• Experience with ML model deployment (MLflow, Airflow, Prefect, etc.).
• Understanding of MLOps practices and model monitoring.
• Experience in domains like demand forecasting, finance, IoT, energy, or supply chain.
• Familiarity with CI/CD and infrastructure as code.