Machine Learning Ops Engineer - San Antonio, Dallas, Austin, Texas

Key Skills

Aartificial intelligence, Data Engineering, Data science techniques, Databricks, DataDog, Google BigQuery, Google Cloud Platform (GCP), Machine learning techniques, PySpark, Python Programming, Scala Programming, SPARK Programming, TensorFlow, XGBoost

Job Description

Responsibilities

Since H-E-B Digital Technology's inception, we've been investing heavily in our customers' digital experience, reinventing how they find inspiration from food, how they make food decisions, and how they ultimately get food into their homes. This is an exciting time to join H-E-B Digital--we're using the best available technologies to deliver modern, engaging, reliable, and scalable experiences to meet the needs of our growing audience. If you enjoy taking on new challenges, working in a rapidly changing environment, learning new skills, and applying it all to solve large and impactful business problems, we want you as part of our team.

Our Partners thrive The H-E-B Way. In the Staff Software Engineer job, that means you have a...

HEART FOR PEOPLE... you can serve as technical authority and coordinate work for a group of engineers

HEAD FOR BUSINESS... you make sound, mature technical judgments that result in significant impact

PASSION FOR RESULTS... you can deliver sweeping technical initiatives with minimal guidance

H-E-B is looking for highly motivated Staff Engineer. Bring your passion and dedication, and there's no telling what you could accomplish. If working in a fast-paced environment that challenges your technical abilities on a day-to-day basis is exciting to you, you have found your new home.

• Assembling large, complex sets of data that meet non-functional and functional business

  requirements.

• Has experience leading a team through End to End development – Data Extraction, Data

  Engineering, Feature Engineering, Testing and training, Model development and deployment.

• Has vast experience and hands-on in building Enterprise level applications.
• Mentoring team of Engineers with Industry’s best practices.
• Identifying, designing, and implementing internal process improvements including redesigning

  infrastructure for greater scalability, optimizing data delivery, and automating manual

  processes.

• An expert at building high quality, scalable and resilient distributed systems.
• Building required infrastructure for optimal extraction, transformation and loading of data from

  various data sources using GCP technologies.

• Improving internal processes through machine learning (ML), including increasing velocity in
• ML model building, reducing time to production, and providing solutions for production-level
• ML challenges such as versioning, lineage, and reproducibility.
• Working with stakeholders including product teams to support their Technical Product needs

  while assisting the data- related technical issues.

• Building Analytics and solutions to enable business users in making time-sensitive and critical

  decisions.

• Has researched and implemented various models including but not limited to DNN, xgBoost,
• Biq Query Arima, Linear Regression, Decision tree
• Has increased accuracy of model and identified and fixed Data drifts and model drifts.
• Build predictive models and machine-learning algorithms using distributed computing for Data Engineering and Training.
• Expertise designing and implementing data pipelines using modern data engineering approach and tools: PySpark, Scala Scala, Dataproc, Databricks etc.
• Focus on analyzing and interpreting data to find insights, trends and patterns.
• Create analytics solutions for businesses by combining various tools, applied statistics, and machine learning
• Lead discussions and initiatives and assess the feasibility of AI/ML solutions for business processes and outcomes
• We are looking for someone with at least four 10+ years of professional experience as a ML Engineer/Data science/Data Engineer, a bachelor’s degree in CS/EE and are self-driven, highly motivated and able to learn quickly.

Technologies

• Python, Scala
• Spark, Ray
• Kubernetes, Docker, Dataform and ML pipelines
• Big Query
• Databricks, Dataproc
• Vertex AI, Bigquery ML, TensonFlow
• Datadog, Relic