Senior Software Engineer(AI/ML Platform)

Autodesk; Pune District; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

Autodesk is seeking an experienced Senior Software Engineer in Pune. This role focuses on the design of an AI/ML serving platform within a hybrid cloud architecture, with responsibilities including developing scalable systems for model serving and overseeing hybrid cloud management across platforms like AWS and Azure. Candidates should possess 5+ years of software development experience, strong AI/ML technology knowledge, and proficiency in programming languages such as Python. Join us to shape the future of our AI/ML capabilities!

Qualifications

• 5+ years of experience in software development and engineering.
• Hands-on experience with AI/ML frameworks such as TensorFlow and PyTorch.
• Strong coding skills in programming languages commonly used in AI/ML.

Responsibilities

• Design and implement scalable AI/ML serving systems.
• Architect and manage a hybrid cloud environment.
• Oversee deployment of AI/ML models into production.

Skills

AI/ML Frameworks

Python

Hybrid Cloud Management

Containerization (Docker)

CI/CD Practices

Education

BS or MS in Computer Science

Tools

Kubernetes

AWS

Azure

Job description

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)
• Prior experience of on‑call rotation for tier‑1 services with 24x7 support mechanism