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Senior Backend Developer
Gnani Innovations —Remote
- Remote
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- Master's degree
- AWS

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Senior Backend Developer

Gnani Innovations
Remote

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Job details

Qualifications

CI/CD
Performance tuning
Cloud infrastructure
Azure
Go
Law
Kubernetes
Software deployment
MongoDB
Master's degree
Microservices
AWS
Docker
Machine learning
PostgreSQL
Distributed systems
Terraform
Continuous integration
Redis
RabbitMQ
Back-end development
REST
APIs
Kafka
AI
Python
Debugging
MySQL

Full job description

Remote

3 - 5 years experience

Responsibilities

We are looking for a highly skilled Senior Backend Developer who can design, build, and scale distributed systems that power high-growth products and AI-driven applications. The ideal candidate has deep expertise in Python and Go, extensive experience with FastAPI, containerized deployments using Docker and Kubernetes, and a proven track record of scaling systems serving millions of requests while maintaining reliability, performance, and security.

This role requires strong architectural thinking, hands-on coding expertise, and the ability to optimize backend systems for high throughput, low latency, and cost efficiency.

Key Responsibilities

Backend Architecture & Development

Design and develop highly scalable backend services using Python and Go.
Build and maintain RESTful APIs and microservices using FastAPI.
Architect distributed systems capable of handling high traffic and large-scale workloads.
Write clean, maintainable, testable, and production-grade code.
Establish engineering standards, code review practices, and backend best practices.

Performance & Scalability

Lead initiatives for system scalability, reliability, and performance optimization.
Identify and eliminate bottlenecks in APIs, databases, caching layers, and infrastructure.
Optimize application latency, throughput, and resource utilization.
Design systems for horizontal scaling and fault tolerance.
Implement caching strategies using Redis and distributed caching mechanisms.

Cloud & Infrastructure

Build and manage containerized applications using Docker.
Deploy, operate, and optimize workloads on Kubernetes clusters.
Design CI/CD pipelines and automated deployment workflows.
Implement infrastructure observability using monitoring, logging, and tracing tools.
Drive cloud cost optimization and infrastructure efficiency initiatives.

AI Platform Development

Develop backend services supporting AI/ML applications and LLM-based products.
Build APIs for AI model inference, orchestration, and integration.
Work with vector databases, embeddings, RAG architectures, and model-serving pipelines.
Optimize AI workloads for performance, scalability, and cost.
Collaborate with AI engineers and data scientists to productionize machine learning systems.

Reliability & Security

Design resilient systems with high availability and disaster recovery considerations.
Implement authentication, authorization, and API security best practices.
Ensure compliance with security standards and data protection requirements.
Conduct architecture reviews and proactively address technical debt.

Required Qualifications

6+ years of backend development experience.
4+ years of hands-on experience with Python.
2+ years of production experience with Go (Golang).
Strong expertise in FastAPI and modern API design principles.
Proven experience scaling applications from startup scale to high-traffic production environments.
Extensive experience with Docker and Kubernetes in production.
Strong understanding of distributed systems, microservices architecture, and event-driven systems.
Deep knowledge of database optimization and query performance tuning.
Experience with PostgreSQL, MySQL, MongoDB, Redis, or similar technologies.
Strong understanding of concurrency, parallel processing, and asynchronous programming.
Strong debugging, profiling, and performance optimization skills.

Preferred Qualifications

Experience building AI-powered products or Generative AI applications.
Hands-on experience with:
LLM integrations (OpenAI, Anthropic, Gemini, etc.)
RAG architectures
Vector databases (Pinecone, Weaviate, Qdrant, Milvus)
AI orchestration frameworks (LangChain, LlamaIndex)
Experience with message queues such as Kafka, RabbitMQ, or NATS.
Experience with cloud platforms (AWS, GCP, Azure).
Knowledge of Infrastructure as Code (Terraform, Pulumi).
Experience with service mesh technologies and Kubernetes operators.
Exposure to MLOps and AI deployment workflows.

Skills Required

Primary Skills

RabbitMQ

Redis

Go(Golang)

Python Programming

MongoDB

FastAPI

Async Programming

Advanced Python

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