We are seeking a highly skilled Senior AI 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 is Hybrid role
Location - Bengaluru / Hyderabad/ Pune/ Gurugram
Shift Timings - 2 PM - 11 PM IST
Monday to Friday
**Looking for Immediate Joiners Only**
7-8 years of backend development experience
Need strong Python and ML frameworks (Tensor Flow, Py Torch) experience
This role bridges backend engineering, machine learning systems, and production infrastructure , ensuring AI solutions are scalable, secure, and production-ready.
Key Responsibilities
Design 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 Dev Ops to manage cloud infrastructure and CI/CD pipelines.
Required Qualifications 7-8 years of backend development experience.
Strong proficiency in:
Python (preferred) or Node.js / Go
RESTful APIs and/or Graph QL
Experience working with ML frameworks (Tensor Flow, Py Torch, 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 No SQL).
Experience with Docker and containerization.
Knowledge of distributed systems and microservices architecture.
Preferred Qualifications
Experience with model serving frameworks (Torch Serve, Fast API, 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.
