Description
Data Engineer - Python & Kubernetes (ML Ops, ML Engineering Team)
Responsibilities
The ML Engineering activities for the Client's Power Digital Core significantly contribute to our data science, data engineering, optimization engineering, orchestration engineering and ML Ops efforts. This experienced engineer will grow and optimize this critical technical foundation for Shell Power Digital Core, and will work across Shell's global power business, suite of portfolio companies, and enterprise Centers of Excellence to deliver outsized value. Responsibilities include:
• Working within the Data Science and ML team to deploy models to production, using existing and emerging methods and technologies that could be effectively applied to Shell use cases
• Building core infrastructure around feature engineering, model training, model deployment, ongoing monitoring tools, and much more.
• Help us build and automate our AI/ML workstream from data analysis, experimentation, operationalization, model training, model tuning to visualization.
• Working closely with Infrastructure, data engineering and DevOp teams to increase our deployment velocity, including the process for deploying models and data pipelines into production
• Working closely with Product Management to translate product requirements into robust, customer-agnostic machine learning architectures
Skills – Required:
• Strong experience in designing, developing, deploying and monitoring machine learning and deep learning solutions.
• Strong experience in MLOps: managing production machine learning lifecycle.
• Experience implementing, Improving and maintaining automated CI/CD pipeline
• Experience working with data scientists and/or ML engineers and building auto-scaling ML systems.
• Development competency across a breadth of languages, frameworks and tooling, such as Python, R, Kubernetes, Kubeflow, JavaScript/JVM, Scala, Akka, Reactive Streams, Kafka, etc.
• Development competency in data engineering and storage, including across breadth of tools such as S3, RDS, EFS, No-SQL, graph db, Spark, etc.
skills - Nice to have:
• Strong desire to stay ahead of industry trends & technologies with a commitment to continuous learning.
• Experience with Machine Learning Concepts - Training, Validation, Testing, Precision/Recall, Bias/Variance etc.
• Experience in extracting, cleansing, and manipulating large, diverse structured and unstructured data sets.
• Knowledge and experience in applying ML algorithms and technologies to time series forecasting
• Experience in energy management domain is a plus, with comfort in energy asset optimization, asset control and data flow loops, and wholesale electricity market applications
• Experience managing people / leading a team
Bonuses to include as part of your application.
• Links to online profiles you use such as Github, Twitter, etc.
• A description of your work history (whether as a resume or LinkedIn profile)