Job Requisition ID #
Position Overview
We are looking for an experienced Senior Software Engineer to join the AMP team, focusing on the design of our AI/ML serving platform within a hybrid cloud architecture. This important role involves architecting scalable, efficient systems for model serving and inference, ensuring seamless deployment and management across diverse environments. You will have a background in software engineering, with an understanding of AI/ML technologies, and experience managing hybrid cloud infrastructures. As an important contributor to our engineering team, you will help shape the future of our AI/ML capabilities, delivering solutions that inspire value for our organization. You will report to Manager. You will work from Pune location, and your jobs workplace type will be hybrid.
Responsibilities
Design and Implement Scalable AI/ML Serving Systems: Develop scalable and efficient systems for serving AI/ML models, ensuring that these systems can handle varying loads and perform with low latency across diverse environments
Hybrid Cloud Architecture Management: Architect and manage a hybrid cloud environment that uses both on-premises resources and multiple cloud platforms (e.g., AWS, Azure, GCP) to optimise performance, cost, and scalability
Model Deployment and Versioning: Oversee the deployment of AI/ML models into production, including the setup of CI/CD pipelines for model deployment and versioning, ensuring smooth and reliable model updates and rollbacks
Performance Monitoring and Optimization: Implement monitoring tools and practices to track the performance of AI/ML models in production, identifying bottlenecks and optimizing system and model performance for better efficiency and reduced costs
Security and Compliance: Ensure that the AI/ML serving systems follow industry standards and regulatory requirements for data security and privacy, including the management of data encryption, access controls, and audit trails
Collaboration and Leadership: Work closely with AI/ML researchers, data engineers, and other partners to translate complex AI/ML models into production-ready systems, providing technical guidance throughout the project lifecycle
Research and Innovation: Stay informed about the latest developments in AI/ML technologies, cloud computing, and software engineering practices, exploring and integrating solutions that can enhance the capabilities and efficiency of the AI/ML serving platform
Minimum Qualifications
Educational Background: BS or MS in Computer Science, or equivalent practical experience
Experience: 5+ years of experience in software development and engineering, with a solid record of delivering production systems and services
Expertise in AI/ML Technologies: Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment
Proficiency in Programming Languages: Strong coding skills in languages commonly used in AI/ML and system development, such as Python
Experience with Cloud Technologies: Experience with designing and managing systems on hybrid cloud architectures, including working knowledge of cloud service providers like Azure
Knowledge of Containerization and Orchestration Tools: Familiarity with containerization technologies (e.g., Docker) and orchestration systems (e.g., Kubernetes), crucial for deploying and scaling applications in a cloud environment
Understanding of DevOps Practices: Knowledge of CI/CD pipelines, infrastructure as code, and other DevOps practices to ensure smooth deployment and operation of AI/ML systems
System Performance Optimization: Deep understanding of performance metrics and latency optimization techniques, with the ability to diagnose, tune, and enhance the efficiency of serving systems
Preferred Qualifications
Cloud Certifications: Certifications in cloud technologies from major providers (AWS Certified Solutions Architect, Google Cloud Professional Cloud Architect, Microsoft Certified: Azure Solutions Architect Expert), indicating a high level of expertise in cloud services and architecture
Experience with Big Data Technologies: Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment
AI/ML Model Monitoring Tools: Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)
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