Senior AI Backend Engineer

• Total Experience : 6 Years
• Location : India
• Shift time : 2 PM to 11 PM
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About the Role

We are seeking a highly skilled SeniorAI Backend Engineer to design, build, and scale backend systems that power AI-driven products. You will work at the core of our AI infrastructure, integrating machine learning models, optimizing inference performance, and building reliable APIs and data pipelines.

This role bridges backend engineering, machine learning systems, and production infrastructure, ensuring AI solutions are scalable, secure, and production-ready.

• Key ResponsibilitiesDesign and develop scalable backend services that support AI-powered applications.
• Build and maintain APIs for model inference (LLMs, NLP, computer vision, recommendation systems).
• Deploy and manage machine learning models in production environments.
• Develop data pipelines for training, evaluation, and real-time inference.
• Optimize system performance for low-latency, high-throughput AI workloads.
• Implement monitoring, logging, and observability for AI services.
• Ensure system security, reliability, and scalability.
• Collaborate with ML engineers to productionize research models.
• Work with DevOps to manage cloud infrastructure and CI/CD pipelines.

Required Qualifications

• 6 years of backend development experience.
• Strong proficiency in:
• Python (preferred) or Node.js / Go
• RESTful APIs and/or GraphQL
• Experience working with ML frameworks (TensorFlow, PyTorch, etc.).
• Experience deploying models to production environments.
• Experience in RAG, LLM and Agentic AI
• Familiarity with cloud platforms (AWS, GCP, or Azure).
• Strong understanding of databases (SQL and NoSQL).
• Experience with Docker and containerization.
• Knowledge of distributed systems and microservices architecture.

• Preferred QualificationsExperience with model serving frameworks (TorchServe, FastAPI, Triton, etc.).
• Knowledge of vector databases (Pinecone, Weaviate, FAISS).
• Experience with streaming systems (Kafka, Pub/Sub).
• Familiarity with Kubernetes.
• Understanding of MLOps practices and tools.
• Experience in high-growth or startup environments.