Backend Engineer

Backend Engineer APIs, Microservices & Data Systems

Location: Bengaluru, India
Work Mode: On-site
Experience: 1-2 Years
Employment Type: Full-time

Job Overview

We are looking for a Backend Engineer with strong Python and backend development skills to design, build, and maintain robust APIs, microservices, and data systems supporting AI and NLP applications.

The role involves backend engineering with a strong focus on performance, scalability, reliability, and integration with ML model inference pipelines.

Key Responsibilities

API & Microservices Development

• Design and develop RESTful and gRPC APIs for AI/NLP system integrations.
• Build scalable microservices using FastAPI, Flask, or Django.
• Implement authentication, authorization, and rate limiting.
• Maintain API versioning, documentation, and backward compatibility.

Data Engineering & Storage

• Design and manage relational databases such as PostgreSQL and MySQL.
• Work with NoSQL technologies including MongoDB and Redis.
• Build data ingestion, transformation, and processing pipelines.
• Work with vector databases such as FAISS, Milvus, and Weaviate for AI-powered search.
• Optimize database queries and indexing for improved performance.

ML System Integration

• Integrate ML model inference endpoints into backend services.
• Build APIs that integrate with NLP, STT, TTS, and translation models.
• Implement asynchronous processing using Celery, RabbitMQ, or Kafka.
• Support streaming responses for real-time inference systems.

Performance & Scalability

• Profile and optimize API latency and throughput.
• Implement caching strategies using Redis or Memcached.
• Design systems for horizontal scalability and fault tolerance.
• Perform load testing and capacity planning.

Deployment & DevOps Collaboration

• Containerize applications using Docker and deploy using Kubernetes.
• Work with CI/CD pipelines such as GitHub Actions and Jenkins.
• Support on-premise and edge deployment requirements.
• Develop unit, integration, and end-to-end tests.

Research & Innovation

• Explore new API design patterns and model-serving frameworks.
• Prototype and evaluate vector databases and ML inference integrations.
• Contribute to internal backend libraries and shared engineering tools.

Collaboration

• Work closely with ML Engineers, Data Scientists, and Research Teams.
• Collaborate with linguistic and annotation teams.
• Document systems, implementations, and technical experiments.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• 1-2 years of backend engineering experience.
• Strong programming skills in Python.
• Familiarity with Go or Java is an added advantage.

Must-Have Skills

Backend & Architecture

• REST and gRPC API design
• Microservices architecture
• Software design patterns
• Clean architecture
• Asynchronous processing and task queues

Frameworks & Technologies

• FastAPI / Flask / Django
• PostgreSQL / Redis / MongoDB
• Celery / RabbitMQ / Kafka
• Docker / Kubernetes
• Git / Linux

Systems & Deployment

• Containerization and CI/CD
• On-premise and offline deployment environments
• API security
• OAuth2 / JWT / API Keys

Good-to-Have Skills

• Event-driven architecture and message streaming
• Vector search and RAG systems
• Real-time APIs using WebSockets or SSE
• FAISS / Milvus / Weaviate
• Airflow / Prefect
• GraphQL
• Elasticsearch

What We're Looking For

• Strong software engineering fundamentals and system design skills.
• Hands-on experience building reliable and scalable backend systems.
• Ability to work in a research-driven and collaborative environment.
• Strong focus on performance, reliability, and clean engineering practices.
• Good communication and technical documentation skills.

What You Will Work On

• Backend systems powering multilingual AI applications.
• API and microservices development for AI/NLP systems.
• ML model and inference pipeline integrations.
• Vector search and RAG-related backend systems.
• Scalable and secure deployment across on-premise, offline, and edge environments.