This role is for one of the Weekday's clients

Salary range: Rs 2500000 - Rs 3500000 (ie INR 25-35 LPA)

Min Experience: 4 years

Location: Bengaluru

JobType: full-time

This role is ideal for a backend engineer who enjoys building AI-first platforms from the ground up. You will work on scalable backend systems that power advanced AI-driven developer tools, collaborating closely with AI/ML researchers and product teams to turn cutting-edge research into reliable, enterprise-ready solutions. The position offers hands-on ownership across system design, AI inferencing, and production deployments, with a strong focus on performance, security, and real-world usability.

• Design, develop, and maintain scalable, robust backend systems that support AI-powered products and developer tools
• Architect and implement advanced Retrieval Augmented Generation (RAG) pipelines and knowledge graph–based systems for domain-specific language models
• Build and optimize self-hosted AI inferencing pipelines, ensuring high performance for on-premise, edge, and enterprise deployments
• Collaborate with AI/ML researchers to translate models, experiments, and prototypes into production-grade backend services
• Design and implement APIs and data pipelines that enable complex AI workflows, integrations, and enterprise use cases
• Support deployment of AI solutions across cloud and on-premise environments, including GPU and accelerator-based setups
• Work closely with founders, product managers, and engineers to convert AI capabilities into meaningful product features
• Write clean, maintainable, and well-tested code while actively participating in design discussions and code reviews
• Continuously optimize system performance, reliability, security, and data integrity for enterprise AI platforms

• A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with 4–5 years of backend engineering experience focused on AI/ML systems
• Strong proficiency in Python and backend frameworks such as FastAPI, Django, or Flask
• Hands-on experience designing and implementing RAG systems, including embeddings, vector databases, and retrieval strategies
• Familiarity with knowledge graph technologies such as Neo4j or FAISS and their application in AI systems
• Experience with self-hosted AI inferencing, including optimization techniques like quantization and pruning
• Practical exposure to deploying AI workloads on GPUs or specialized AI accelerators
• Solid understanding of system design, data structures, algorithms, and distributed backend architectures
• An ownership-driven mindset with strong problem-solving skills and the ability to work effectively in a fast-moving environment
• Bonus points for experience with enterprise on-premise deployments, AI security and compliance practices, MLOps, LLM fine-tuning, or startup environments