Backend Engineer - AI/ML Deployment

As a Backend Engineer at Jio Reality Labs, your role involves designing, deploying, and managing scalable AI/ML pipelines to support machine learning applications for millions of users. You will work on deploying open-source models and implementing reinforcement learning solutions. Here's what you'll be responsible for: - AI/ML Pipeline Development and Deployment: - Design and implement end-to-end pipelines for deploying open-source machine learning models, focusing on scalability, performance, and monitoring. - Collaborate with data science teams to build reinforcement learning pipelines, managing real-time policy updates, feedback loops, and continuous model improvement. - Open-Source Model Management: - Deploy and optimize models using frameworks like TensorFlow, PyTorch, and ONNX for efficient resource usage and scalable deployment. - Containerize models with Docker and orchestrate deployments with Kubernetes to ensure consistency and reliability across environments. - Reinforcement Learning (RL) Implementation: - Architect and manage RL pipelines for real-time data ingestion, policy updates, and action feedback loops to enhance decision-making in AI-driven applications. - Support large-scale training and deployment of RL agents, leveraging distributed systems for timely updates and optimized performance. - Backend Engineering & Cloud Infrastructure: - Use cloud platforms (AWS, GCP, Azure) to build scalable, high-performance infrastructure supporting ML workloads. - Implement automated scaling, load balancing, and optimization to handle millions of user requests with low latency. - MLOps and CI/CD Automation: - Develop CI/CD pipelines for streamlined model deployment, monitoring, and updates in production environments. - Utilize monitoring tools like Prometheus and Grafana to track model performance, system health, and ensure rapid troubleshooting. - Data Security and Compliance: - Apply best practices in data security and compliance, including encryption and user data anonymization, to adhere to industry standards and protect customer data. Qualifications: - Bachelors or Masters Degree in Computer Science, Engineering, or related field. - 3+ years of experience in backend engineering deploying scalable AI/ML pipelines. - Proficiency in deploying open-source models with TensorFlow, PyTorch, or ONNX. - Expertise in reinforcement learning pipelines with real-time policy updates. - Strong knowledge of containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). - Experience in CI/CD, MLOps, and data security for compliance. At Jio Reality Labs, you'll have the opportunity to work with cutting-edge AI/ML technologies, in a collaborative and dynamic work environment that encourages continuous learning and growth. We offer flexible work hours, a competitive salary, and benefits package. If you are passionate about advanced AI technologies and have experience in deploying high-performance ML systems, we encourage you to apply with your resume and a cover letter detailing your experience with open-source model deployment and reinforcement learning. As a Backend Engineer at Jio Reality Labs, your role involves designing, deploying, and managing scalable AI/ML pipelines to support machine learning applications for millions of users. You will work on deploying open-source models and implementing reinforcement learning solutions. Here's what you'll be responsible for: - AI/ML Pipeline Development and Deployment: - Design and implement end-to-end pipelines for deploying open-source machine learning models, focusing on scalability, performance, and monitoring. - Collaborate with data science teams to build reinforcement learning pipelines, managing real-time policy updates, feedback loops, and continuous model improvement. - Open-Source Model Management: - Deploy and optimize models using frameworks like TensorFlow, PyTorch, and ONNX for efficient resource usage and scalable deployment. - Containerize models with Docker and orchestrate deployments with Kubernetes to ensure consistency and reliability across environments. - Reinforcement Learning (RL) Implementation: - Architect and manage RL pipelines for real-time data ingestion, policy updates, and action feedback loops to enhance decision-making in AI-driven applications. - Support large-scale training and deployment of RL agents, leveraging distributed systems for timely updates and optimized performance. - Backend Engineering & Cloud Infrastructure: - Use cloud platforms (AWS, GCP, Azure) to build scalable, high-performance infrastructure supporting ML workloads. - Implement automated scaling, load balancing, and optimization to handle millions of user requests with low latency. - MLOps and CI/CD Automation: - Develop CI/CD pipelines for streamlined model deployment, monitoring, and updates in production environments. - Utilize monitoring tool