Senior Software Engineer(AI/ML Platform)
Autodesk · Pune · 5+ yrs experience · Posted 2026-07-18
Tech stack: AWS, Azure, Docker, GCP, Google Cloud, Kafka, Kubernetes, Python
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About the role
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
Qualifications:
- Educational Background:
- BS or MS in Computer Science, or equivalent practical 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
- 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)
- 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
- 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)
- 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)
Qualifications
- Educational Background: BS or MS in Computer Science, or equivalent practical 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 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)
- 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
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