Senior AI Backend Engineer

About the Role

We are seeking a highly skilled Senior AI Backend Engineer to design, build, and scale backend systems powering our next-generation AI-driven products. You will operate at the core of our AI infrastructure, integrating cutting-edge machine learning models (LLMs, NLP, Computer Vision), optimizing inference performance for low-latency workloads, and building robust APIs and data pipelines.

Key Responsibilities

• Design and develop scalable backend services supporting advanced AI-powered applications.
• Build and maintain high-performance RESTful/GraphQL APIs for model inference serving state-of-the-art LLMs and recommendation systems.
• Deploy, manage, and monitor machine learning models in production environments with a focus on reliability and security.
• Develop efficient data pipelines for training, evaluation, real-time inference, and RAG implementations.
• Optimize system performance to handle high-throughput AI workloads using microservices architecture.
• Implement comprehensive monitoring, logging, and observability solutions for AI services.
• Collaborate closely with ML engineers to productionize research models and ensure seamless integration with DevOps teams for CI/CD pipelines on AWS, GCP, or Azure.

Requirements

We are looking for immediate joiners only.

• Experience: 7-8 years of backend development experience (Senior Level).
• Languages/Frameworks: Strong proficiency in Python (preferred) or Node.js/Go; extensive hands-on experience with TensorFlow, Keras, PyTorch.
• API & Architecture: Deep knowledge of RESTful APIs, GraphQL, distributed systems, and microservices architecture using Docker/Kubernetes.
• AI Specialization: Proven track record in deploying models to production; practical experience with RAG (Retrieval-Augmented Generation), LLMs, Agentic AI, vector databases (Pinecone, Weaviate, FAISS), and model serving frameworks like TorchServe or Triton.
• Data & Cloud: Strong understanding of SQL/NoSQL databases; experience with streaming systems (Kafka) and cloud platforms (AWS, GCP, Azure).