Python Microservices Developer - Hybrid

NTT DATA Services•Pine Grove Township, PA

4d•$75,168 - $130,500•Hybrid

About The Position

We are seeking a Python Microservices Developer who is passionate about building scalable, high‑performance services and advancing modern AI‑driven architectures. In this role, you will design microservices, develop generative AI capabilities, and work across a modern cloud‑native stack. If you enjoy solving complex engineering challenges, working with cutting‑edge frameworks, and contributing to a collaborative, fast‑moving team, this opportunity will allow you to make a meaningful impact.

Requirements

5+ years of overall software engineering experience.
3+ years of experience building Python‑based microservices in production environments.
2+ years of hands‑on experience with asynchronous Python frameworks such as FastAPI or aiohttp.
Strong proficiency with SQL and NoSQL databases, including experience with ORMs like SQLAlchemy.
Familiarity with cloud platforms (AWS, Azure, or GCP) and infrastructure‑as‑code principles.
Solid understanding of distributed system design patterns (CQRS, Event Sourcing, Circuit Breakers).
Experience with message‑driven and event‑driven architectures.
Strong understanding of containerization, orchestration, and modern DevOps practices.
Excellent problem‑solving skills and the ability to design scalable, maintainable solutions.

Nice To Haves

Experience with Chat, IVR, or Banking systems is a plus.
Knowledge of Google Cloud Platform (GCP) is an asset.

Responsibilities

Design, implement, and optimize generative AI models using TensorFlow, PyTorch, or JAX, including architectures such as GANs, VAEs, and LLMs.
Develop and deploy efficient, self‑contained microservices using Python 3.x and frameworks like FastAPI, Flask, or Django.
Build and maintain RESTful APIs and GraphQL endpoints to support seamless communication across services and applications.
Work with relational databases (PostgreSQL, MySQL) and NoSQL systems (MongoDB, Redis), implementing database‑per‑service patterns.
Implement message queues and event‑driven architectures using Kafka, RabbitMQ, or Celery.
Containerize microservices using Docker and orchestrate deployments with Kubernetes.
Write comprehensive unit and integration tests; configure logging and monitoring using Prometheus, Grafana, or the ELK stack.
Select appropriate datasets, data representations, and preprocessing strategies for model development.
Extend existing machine learning libraries and frameworks to support new features and use cases.
Train and retrain machine learning systems as needed to maintain performance and accuracy.
Collaborate with cross‑functional teams to ensure scalable, reliable, and maintainable service design.