Python Developer - Code Ninja

Overview

About Vaanee AI

Vaanee AI is an advanced speech technology company building next-generation solutions in multilingual voice cloning, AI-based dubbing, audio post-production automation, and intelligent media workflows. We operate at the intersection of deep learning, high-performance backend systems, and scalable infrastructure to deliver mission-critical, production-grade systems for the media and entertainment industry.

We are looking for a Senior Python Developer who demonstrates mastery over system-level programming constructs, cloud-native design, asynchronous APIs, and high-throughput model inferencing. You will be a part of a highly technical, performance-obsessed engineering team building products for the real-world scalability of artificial intelligence.

Role Summary

The Senior Python Developer will be responsible for architecting and implementing backend services that interface directly with machine learning models and handle high-concurrency workloads. The role requires end-to-end ownership of services, including performance optimization, containerized deployments, and multi-threaded processing pipelines.

Key Responsibilities

• Architect and implement RESTful APIs using Django, Flask, or FastAPI from scratch.
• Build scalable and thread-safe services to handle model inferencing and data processing.
• Design and manage distributed job queues using RabbitMQ, SQS, or ActiveMQ.
• Leverage multi-processing and asynchronous patterns to ensure application responsiveness.
• Integrate cloud-native services from AWS, Azure, or GCP for compute, queue, and storage management.
• Deploy containerized applications using Docker and orchestrate via Kubernetes.
• Apply performance profiling techniques to optimize memory, latency, and throughput.
• Develop robust CI/CD pipelines with linting, testing, and integration stages.
• Work closely with AI/ML engineers to productionize models.
• Implement GPU offloading and concurrency management in hybrid compute environments.
• Maintain exceptional code quality, documentation, and system-level reliability.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related discipline.
• 4+ years of Python programming experience, with production-grade backend development.
• Proven expertise in multithreading, multiprocessing, and asynchronous programming.
• Demonstrable experience with one or more Python frameworks (FastAPI, Flask, Django).
• Strong understanding of distributed systems and event-driven architecture.
• Proficient with Docker, Kubernetes, and deployment pipelines.
• Familiarity with REST API security, caching, and throttling strategies.
• Exposure to high-performance GPU applications and memory management.
• Experience with RabbitMQ, ActiveMQ, or Amazon SQS/SNS.
• Prior work on high-throughput, low-latency systems preferred.

Preferred Skills

• Experience with CUDA, TensorRT, or Triton Inference Server.
• Understanding of advanced Python features: memoryview, ctypes, asyncio, and context managers.
• Prior exposure to MLOps and serving ML models in production.
• Familiarity with microservices architecture, API gateway management, and observability tools (Prometheus, Grafana).
• Ability to write and maintain performance benchmarks and test suites.

Benefits

• Competitive compensation package with ESOP opportunities.
• Remote-first environment with flexible work hours.
• Exposure to cutting-edge AI systems and tools.
• Opportunity to work with an elite team of AI, infrastructure, and media professionals.
• Access to wellness programs, including confidential support systems.

Job Types: Full-time, Permanent

Pay: ₹12,885.50 - ₹64,902.56 per month

Benefits:

• Work from home

Schedule:

• Day shift

Supplemental Pay:

• Performance bonus

Application Question(s):

• In Python, what’s the difference between multithreading and multiprocessing? Why might multithreading fail to speed up a CPU-bound task in CPython?
• Explain the steps and key components involved in developing a secure REST API using FastAPI. What precautions would you implement in production?
• A Docker container running your Python API crashes after 30 seconds in Kubernetes, but runs locally. What are your first 3 debugging steps?

Work Location: Remote