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Principal Software Engineer
1 month ago
We are seeking a highly skilled and experienced software engineer to develop our next-generation AI/ML platform used in the development of Autodesk's suite of products and services.
Key Responsibilities:- Lead the design and engineering of software systems for the AI/ML Platform, contributing to the full ML development lifecycle.
- Develop comprehensive systems to automate and optimize laborious processes, integrating them seamlessly into our platform to streamline operations.
- Develop tools for building and deploying ML artifacts in production environments, facilitating a smooth transition from development to deployment.
- Automate and orchestrate tasks related to managing big data transformation and processing, building large-scale data stores for ML artifacts.
- Collaborate across diverse teams, including machine learning researchers, developers, product managers, software architects, and operations, fostering a collaborative and cohesive work environment.
- Take ownership of critical components of the platform, providing architectural direction, and contributing to the overall success of the AI/ML Platform.
- BSc in Computer Science, or equivalent practical experience.
- Over 8 years of experience in software development and engineering, with a solid record of delivering production systems and services.
- Expertise in programming languages such as Python, Java, Go, scripting languages, and SQL.
- In-depth experience with Amazon AWS (Amazon Web Services) or Azure cloud technologies.
- Excellent track record in scalable system design and distributed software architecture.
- In-depth experience working with big data technologies, including NoSQL, Hadoop, Spark, Hive, and data pipelines.
- Strong expertise in data platforms, encompassing the design and implementation of scalable and efficient data storage, retrieval, and processing systems.
- Familiarity with agile development methodologies, including CI/CD & test-driven development.
- Working knowledge with cloud data processing, training, deployment, or operations, such as Snowflake or Databricks.
- Working knowledge of cloud networking principles and their security implications for organizations holding sensitive data.
- Exposure to deploying ML-enabled projects and solutions to production environments.
- Familiarity with Machine Learning Operations practices.
- Exposure to open-source Large Language Models on Hugging Face like Llama & Mixtral.
- Exposure to statistical analysis.
- Past collaboration with data scientists and researchers.
- Hands-on experience building on Kubernetes-centric infrastructure and CI/CD processes.
