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AVP Machine Learning Operations Engineering 

resume-library  |  United States  |  

United StatesUnited States (US)
Work Type:
Work Time:
Full Time


AVP Machine Learning Operations Engineering


Lead a team of ML Engineers with experience in Software Engineering, Data Science, Data Engineering, and System Architecture to design, develop, test, launch, and maintain advanced MLOps capabilities in Azure. This leadership position requires hands-on experience in ML pipelines, ML development, streaming analytics, software development, APIs, ML Flow, microservices, data dev ops, data dev security, and event-driven cloud architectures, preferably using Azure services and supporting capabilities such as databricks. Key responsibilities include enabling ML model scalability, management, robustness, reusability, reproducibility, compliance, and responsible AI. This technical and people leadership position requires expertise and passion for building agile teams to plan effectively, collaborate with broader cross-functional teams, and successfully deliver mission-critical data and analytics projects. It requires developing strong partnerships with other leaders, team members, and vendors to scale MLOps and contribute to the development and evolution of an enterprise data and analytics architecture.



Build and sustain a high-performing team of Data Scientists, Data Engineers, Software Engineers, and System Architects to develop enterprise-wide and scalable cloud-based MLOps capabilities that span the full life cycle of analytical models

Develop reusable, secure, and robust ML pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all ML life cycle activities This includes developing code and making sure that ML models are production ready

Continuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance

Collaborate with Model Management/Governance to develop and maintain enterprise wide MLOps standards

Collaborate with internal stakeholders and vendors in developing MLOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, cybersecurity, compliance, and Legal

Maintain up to date knowledge about the latest advances in MLOps, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative MLOps capabilities

Develop and maintain and deep understanding of business requirements to ensure that MLOps solutions deliver practical and timely value

Conduct MLOps research projects to improve practice and develop business cases that support business needs

Develops and apply algorithms that generate success metrics to improve the value of MLOps

Presents findings and analysis for use in decision making

Collaborate with other Cloud Solution Architects in developing solutions on the Microsoft Azure platform

Be an Azure platform evangelist with customer, partners, and external entities

Prioritizes tasks and meets project deadlines in a fast paced work environment

Perform other duties as assigned

Conform with all company policies and procedures



Experience in full ML life cycle automation that includes data ingestion, data validation, data and source versioning, attribute lineage, feature engineering, evaluation of model experiments, model training, model validation in release pipelines, assessing responsible AI, model registration, containerized deployment, event-driven monitoring, and integration with ML Flow pipelines

Ability to understand and clearly articulate trade-offs of various approaches to solving machine learning platform problems

Experience with messaging technologies such as Azure Event Hubs, Azure Event Grid, Azure Service Bus, Kafka, RabbitMQ, and others

Strong knowledge in Azure DevOps or equivalent including Github, Boards, CI/CD and other related functionality

Experience in agile methos, preferrable using the SAFe agile framework

Strong knowledge and experience with software engineering practices

Significant, hands-on experience supporting large data sets

Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics

Ability to identify and understand business issues and map these issues into operational and quantitative questions

Demonstrated understanding of applied analytical methodologies including Decision Trees, Neural Networks, Regression, NLP, and others

Advanced skills in Python, SQL, Excel, Word, and PowerPoint JavaScipt, SAS, and C# are highly desirable

Ability to design and implement model documentation and monitoring protocols

Comprehensive knowledge and experience with technical systems, datasets, data warehouses, analytical database, and data analysis techniques


Ability, drive, and curiosity to quickly learn and stay up to date on technological and analytical advances

Applied expertise in the Azure ecosystem with particular emphasis on data and Machine Learning

Experience in Azure, Azure ML, databricks, or equivalent such as AWS and GCP, MLOps, data architectures, container orchestration, streaming, H2O, Spark, and Linux

Fluency in Python and SQL with a demonstrable history of writing clean, clear code as part of a team, Expertise in other languages such as Java, C# and JavaScrip to extend Azure capabilities is highly desirable

Experience in business intelligence tools like Azure Synapse, Snnowflake, and similar technology Experience in PowerBI highly desirable

Experience developing and deploying ML models in management platforms, like MLFlow, Azure ML, etc

Experience in structured, semi-structured, and unstructured data modeling and analysis (RDBMS, Columnar data, JSON, etc)

Working knowledge of networking concepts including TCP/IP, subnetting, routing, DHCP, and others

Understanding of cybersecurity principles

Ability, drive, and curiosity to learn how the business works and develop a deep understanding of business needs

Ability to establish and influence technical and business partners

Ability to solve problems creatively and collaboratively


Bachelor’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required

Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred


7-10 years In state of the art IT and/or Analytics required

7-10 years leadership experience required

5-7 years experience managing IT, software engineering, analytics teams or equivalent required

3-5 years experience developing and deploying cloud-based data and analytics solutions, MLPipelines, and containerization, preferably in Azure required