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Deloitte Edge ML Operations Engineer Senior Consultant in Seattle, Washington

Edge ML Operations Engineer

The Team

Analytics & Cognitive

In this age of disruption, organizations need to navigate the future with confidence, embracing decision making with clear, data-driven choices that deliver enterprise value in a dynamic business environment.

The Analytics & Cognitive team leverages the power of data, analytics, robotics, science and cognitive technologies to uncover hidden relationships from vast troves of data, generate insights, and inform decision-making. Together with the Strategy practice, our Strategy & Analytics portfolio helps clients transform their business by architecting organizational intelligence programs and differentiated strategies to win in their chosen markets.

The Edge ML Operations Engineer will work with our clients to:

  • Create and develop in ML Operations Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases

  • Design build, and develop state of art machine Learning system infrastructure core components and architecture of a machine learning platform to create, train and deploy ML models

  • Automate the day-to-day operational support for model training and model serving pipelines

  • Create monitoring solutions that allow effective system accuracy, performance and enable troubleshooting of production ML models.

  • Identify gaps and evaluate relevant tools and technologies as needed to improve processes and systems, leveraging open-source and cloud computing technologies to build effective solutions.

  • Collaborate with data scientists, data engineers, product teams, and other key stakeholders and drive ML platform projects from conception to completion and production monitoring.

Qualifications:

Required:

  • 3+ years of relevant Analytics consulting or industry experience

  • 3+ years strong experience in large scale distributed systems, Data Engineering, ML Operations, Machine Learning and Data Science areas

  • 2+ years' experience developing data pipelines and orchestrating the deployment of ML models for production ready systems .

  • 1+ year's experience with ML Operations tools such as KubeFlow, MLFlow, Metaflow, or Sagemaker

  • 2+ years of experience leading workstreams or small teams.

  • Demonstrated expertise with one full life cycle analytics engagement across strategy, design and implementation.

  • BS or MS in Computer Science, Computer Engineering or similar field

  • Ability to travel up to 50% on average, based on the work you do and the clients and industries/sectors you serve

  • Limited immigration sponsorship may be available

Preferred:

  • Familiarity working with Tensorflow/Tensorflow Lite or other similar on device/edge inference frameworks and/or optimizing C++ algorithms to run on high performance edge computing platforms with GPU, DSP or neural processors

  • Strong understanding of containerization (Docker) and container-orchestration systems like Kubernetes; experience with data workflow managers such as Airflow is a plus

  • Experience with AWS service and a solid understanding of VPC, ALB/ELB, EC2, Route53, Kinesis, IAM, and other AWS concepts.

  • Experience with stream processing technology Kafka, Spark, Samza, Flink, etc

  • Infrastructure as code - Terraform experience

  • Experience building and optimizing 'big data' data pipelines, architectures, and data sets

  • Experience supporting and working with cross-functional teams in an agile environment.

  • Experience in the operationalization of Data Science projects (MLOps) using AWS or Google or Azure

  • Good understanding of ML and AI concepts and hands-on experience in ML model development

    All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.

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