Machine Learning Engineer- Cloud Deployment

We are looking for an exceptional Machine Learning Engineer to design, build, operationalize, and scale production-grade AI/ML and Agentic AI systems at Autodesk For Go-to-market intelligence function. The mission of the team is to empower decision makers and the broader data communities through trusted data assets and scalable self-serve intelligence. The focus of this role will be engineering end-to-end AI/ML solutions—including feature engineering, data cleansing, contributing in model training process, model deployment, model validations, model evaluation, inference pipelines, and production orchestration. You will work at the intersection of machine learning, data & analytics engineering, You will collaborate closely with data engineers, data scientists, analysts, platform teams, and business stakeholders to deliver reusable intelligent data products at enterprise scale. The role requires a strong engineering mindset, hands-on experience building production ML systems, and the ability to evaluate and integrate rapidly evolving AI technologies while maintaining high standards for quality, observability, security, governance, cost efficiency, and operational reliability. Design, develop, test, deploy, and maintain production-grade ML pipelines supporting enterprise-scale use cases Develop reusable ML services and components Design automated evaluation pipelines using deterministic metrics, model-based evaluation, curated datasets, regression testing, and human evaluation where appropriate Build and maintain distributed processing pipelines capable of handling large volumes of documents, web content, structured data, and unstructured data efficiently Design and optimize distributed pipeline and cloud orchestration for large-scale AI workloads using appropriate workflow orchestration and cloud-native technologies Optimize AI/ML systems for latency, throughput, scalability infrastructure utilization, and model inference cost Partner with platform engineering teams to integrate AI applications with the relevant platforms, APIs, identity and access management, monitoring, and deployment infrastructure Implement appropriate MLOps and LLMOps practices, including model and prompt versioning, experiment tracking, evaluation, deployment automation, monitoring, rollback mechanisms, and lifecycle management Build comprehensive observability and monitoring mechanisms across ML pipelines, covering pipeline health, model performance, data quality, failures, and cost Implement mechanisms to identify and manage model drift, data drift, quality degradation, and upstream data changes Build modular frameworks and reusable components that enable teams to develop new capabilities through self-service patterns rather than one-off implementations Work closely with data scientists, data engineers, analysts, product teams, and business stakeholders to translate business problems into appropriate ML architectures and implementation strategies Translate complex ML system designs, model behavior, limitations, and trade-offs into business-appropriate representations for technical and non-technical stakeholders Contribute to engineering standards, reference architectures, design reviews, code reviews, technical documentation, and AI/ML engineering best practices Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Information Systems, or a related technical discipline 5+ years of machine learning engineering, or data engineering, or related experience, including significant experience developing production systems Demonstrated experience designing and operating production ML systems rather than only experimentation or notebook-based model development Strong programming skills in Python, with the ability to develop modular, testable, maintainable, and production-quality software Working experience with Snowflake, Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe. Have experience on Data transformation tools like DBT Hands-on experience building and deploying machine learning inference pipelines and services Experience designing distributed data or ML processing pipelines for high-volume workloads Experience deploying workloads into a major cloud environment, preferably AWS, and working with cloud services for compute, storage, event processing, monitoring, and distributed execution Experience with Git-based software development workflows, code reviews, branching strategies, and collaborative engineering practices Familiarity with MLOps concepts, including experiment tracking, model lifecycle management, deployment, model monitoring, reproducibility, and versioning Experience working with structured and unstructured data and designing preprocessing, enrichment, and transformation pipelines. Strong analytical, debugging, and problem-solving skills with the ability to diagnose issues across application, model, pipeline, and infrastructure layers Strong writte