Senior Machine Learning Operations Engineer

Key Skills

JavaScript, Java Programming, Management, Python Programming, Database, Framework, SQL, DevOps, RDBMS, Apache Kafka, Scala Programming, Machine learning techniques, Data Visualization, NoSQL, Kubernetes-K8s, Continuous Integration & Continuous Delivery - CI/CD, GraphQL, JVM, R Programming, Algorithms, Kubeflow, SPARK Programming, AWS Sagemaker, Databricks, Amazon Simple Storage Service (S3), Data Lake, Data pipelines

Job Description

The ML Engineering activities for the Digital delivery team 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 client Power Digital Core, and will work across client’s global power business, suite of portfolio companies, and enterprise Centers of Excellence to deliver outsized value..

This role is based in Houston, Texas.

Your future duties and responsibilities:

• Deploy models to production, using existing and emerging methods and technologies that could be effectively applied to use cases
• Architect/build core infrastructure around feature engineering, model training, model deployment, ongoing monitoring tools, and much more.
• Design, build and automate our AI/ML workstream from data analysis, experimentation, operationalization, model training, model tuning to visualization.
• Collaborate 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
• Build enterprise ML strategy to simply the ML model deployment, data pipeline, model management and ML orchestration.

Required qualifications to be successful in this role:

• 5+ years of years of experience in designing, developing, deploying and monitoring machine learning and deep learning solutions.
• 2+ years of experience in MLOps: managing production machine learning lifecycle.
• 2+ years of experience in AWS SageMaker
• Experience implementing, Improving and maintaining automated CI/CD pipeline
• Experience working with data scientists and/or ML engineers and building auto-scaling ML systems.
• Experience in Kubernetes architecture, future store, model management, ML orchestration, PCD and IoT
• Experience in S3, ADLS, Dremio, delta lake, data bricks and time series data base.
• 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.
• 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.
• Proven leadership experience.
• Experience managing people / leading a team.