Vacancy expired!
- 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.
- 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
- Travel up to 50% of the time (Monday - Thursday/Friday). (While 50% of travel is a requirement of the role, due to COVID-19, non-essential travel has been suspended until further notice.)
- 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
Vacancy expired!