Senior Data Scientist

As a Backend AI Engineer, your role will involve designing, developing, and maintaining backend services and APIs to support AI/ML applications at scale. You will be responsible for productionizing ML and deep learning models, ensuring efficiency, reliability, and low-latency inference. Additionally, you will build scalable microservices and REST/gRPC APIs for deploying AI solutions in enterprise environments. Your tasks will also include optimizing AI model serving using frameworks such as TensorRT, ONNX Runtime, Triton Inference Server, or FastAPI. Implementing MLOps practices including model versioning, monitoring, and automated deployments will be part of your responsibilities. Collaboration with AI researchers, data scientists, and product managers to translate prototypes into production-ready systems is essential. Ensuring robustness, fault tolerance, and security in backend AI systems, as well as integrating AI services with enterprise data platforms, cloud systems, and third-party APIs are crucial aspects of the role. You will contribute to architecture discussions, design reviews, and performance tuning, while also mentoring junior engineers and contributing to best practices in AI software engineering. Qualifications Required: - Bachelors degree with at least 7 years of experience or Masters with at least 4 years of experience in Computer Science, Software Engineering, Data Science, or related fields. - Preferred to have a degree from a Tier-1/2 institute (IIT/IISc/NITs if studied in India) or a globally top-ranked university (as per QS). Technical Requirements: - Strong proficiency in Python and backend frameworks (FastAPI). - Expertise in Prompt engineering and working with various LLMs. - Experience in productionizing AI/ML models with efficient inference pipelines. - Hands-on experience with model deployment frameworks (Triton, TensorRT, TorchServe, ONNX Runtime). - Knowledge of cloud platforms (Azure, GCP) and container technologies (Docker). - Strong experience with microservices architecture, CI/CD pipelines, and monitoring tools (Prometheus, Grafana). - Familiarity with databases (SQL/NoSQL) and scalable data storage solutions. - Exposure to LLMs, SLMs, and GenAI model integration into backend systems is a strong plus. - Understanding of security, authentication, and performance optimization in large-scale systems. - Experience with version control (Git) and Agile development practices. In this role, excellent problem-solving skills, attention to detail, strong written and verbal communication skills in English, and the ability to work collaboratively in cross-functional teams are necessary. A passion for building reliable backend systems that bring AI models into real-world impact will be a key driver of success. As a Backend AI Engineer, your role will involve designing, developing, and maintaining backend services and APIs to support AI/ML applications at scale. You will be responsible for productionizing ML and deep learning models, ensuring efficiency, reliability, and low-latency inference. Additionally, you will build scalable microservices and REST/gRPC APIs for deploying AI solutions in enterprise environments. Your tasks will also include optimizing AI model serving using frameworks such as TensorRT, ONNX Runtime, Triton Inference Server, or FastAPI. Implementing MLOps practices including model versioning, monitoring, and automated deployments will be part of your responsibilities. Collaboration with AI researchers, data scientists, and product managers to translate prototypes into production-ready systems is essential. Ensuring robustness, fault tolerance, and security in backend AI systems, as well as integrating AI services with enterprise data platforms, cloud systems, and third-party APIs are crucial aspects of the role. You will contribute to architecture discussions, design reviews, and performance tuning, while also mentoring junior engineers and contributing to best practices in AI software engineering. Qualifications Required: - Bachelors degree with at least 7 years of experience or Masters with at least 4 years of experience in Computer Science, Software Engineering, Data Science, or related fields. - Preferred to have a degree from a Tier-1/2 institute (IIT/IISc/NITs if studied in India) or a globally top-ranked university (as per QS). Technical Requirements: - Strong proficiency in Python and backend frameworks (FastAPI). - Expertise in Prompt engineering and working with various LLMs. - Experience in productionizing AI/ML models with efficient inference pipelines. - Hands-on experience with model deployment frameworks (Triton, TensorRT, TorchServe, ONNX Runtime). - Knowledge of cloud platforms (Azure, GCP) and container technologies (Docker). - Strong experience with microservices architecture, CI/CD pipelines, and monitoring tools (Prometheus, Grafana). - Familiarity with databases (SQL/NoSQL) and scalable data storage solutions. - Exposure to