Job Requisition ID #

24WD76641

Position Overview

We are looking for an experienced Senior Software Engineer to join our platform team focusing on AI/ML Platform (AMP). This team builds and maintains central components to fast track the development new ML/AI models such as model development studio, feature store, model serving, model observability. Ideal candidate would have background in MLOps, Data engineering, DevOps with the experience of building high scale deployment architectures, observability. 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.

Responsibilities

• System design: You will design, implement and manage software systems for the AI/ML Platform, orchestrating the full ML development lifecycle for the partner teams

• Multi-cloud architecture: Define components which leverages strengths from multiple cloud platforms (e.g., AWS, Azure) to optimize performance, cost, and scalability

• Workflow Automation: You will develop comprehensive systems to automate and optimize laborious processes, integrating them seamlessly into our platform to streamline operations

• AI/ML observability: You will build systems for monitoring performance of AI/ML models and finding insights on the underlying data such as drift detection, data fairness/bias, anomalies

• ML Solution Deployment: You will develop tools for building and deploying ML artifacts in production environments, facilitating a smooth transition from development to deployment

• Big Data Management: Automate and orchestrate tasks related to managing big data transformation and processing, building large-scale data stores for ML artifacts

• Scalable Services: Design and implement low-latency, scalable prediction, and inference services to support the diverse needs of our users

• Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, developers, product managers, software architects, and operations, fostering a collaborative and cohesive work environment

• End-to-end ownership: You will take the end-to-end ownership of the components including design, architecture, implementation, rollout, onboarding support to partner teams, production on-call support, testing/verification, investigations etc.

Minimum Qualifications

• Educational Background: Bachelors degree in Computer Science or equivalent practical experience

• Experience: Over 5 years of experience in software development and engineering, delivering production systems and services

• Ability to fluently translate the design into functional code in programming language(any one) Python, Golang, Java

• Knowledge of DevOps practices, containerization, orchestration tools such as CI/CD, Terraform, Docker, Kubernetes

• Prior collaborations with Data science teams to understand ML terminologies, understanding their pain-points

• Demonstrated creative problem-solving skills with the ability to break down problems into manageable components

• Knowledge of Amazon AWS or Azure cloud (beyond EC2, S3 or equivalent)

• Excellent communication and collaboration skills, fostering teamwork and effective information exchange

Preferred Qualifications

• Experience in latency optimization with the ability to diagnose, tune, and enhance the efficiency of serving systems

• Prior experience working on MLOps projects such as ML model serving, CI/CD for ML models, feature engineering pipelines, data quality/lineage pipelines

• Track record in scalable system design and distributed software architecture using big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment

• Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance

• Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)

• Working knowledge with cloud data processing and querying such as Snowflake or Databricks

• Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn

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About Autodesk

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