Senior Machine Learning Engineer

Position Overview 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
• Develop robust model evaluation frameworks covering dimensions such as accuracy, relevance, groundedness, consistency, latency, throughput, robustness, and cost
• 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
• Implement resilient processing patterns including concurrency management, queue-based architectures, checkpointing, retries, failure recovery, rate limiting, and idempotent processing
• 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
• Support experimentation and rapid prototyping while ensuring successful solutions can transition into maintainable, production-grade systems
• Contribute to engineering standards, reference architectures, design…