Master Engineer--AWS MLOps

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The Master Engineer should be proficient in in development of all aspects of data processing including data warehouse architecture/modeling and ETL processing. The successful candidate will work on preparing and deploying ML models, this includes working closely with data scientists, data engineers and the business to understand the data and model deployment requirements. In this role, candidate will apply creativity and problem-solving skills to make massive amounts of data available for machine learning platform and thus bring bank's vision into reality.

Responsibilities

• Build and maintain data pipelines to serve ML models.
• Deploy prediction, reporting decision-making tools that serve business needs.
• Handle various ad hoc requests and debug projects.
• Interact with data engineering and data science teams to identify the requirements for model deployment.
• Help instantiate a robust CICD for all deployed ML models.

The duties listed above are the essential functions, or fundamental duties within the job classification. The essential functions of individual positions within the classification may differ. Texas Capital Bank may assign reasonably related additional duties to individual employees consistent with standard departmental policy.

Qualifications

• Work experience in AWS based ML OPS and Devops environments
• 10+ years Data Engineering experience
• Proficiency in OOP, Python, SQL and at least one structured language like Java Deep Knowledge in cloud platforms (AWS), git and CICD concepts.
• Proficiency in AWS compatible ML Technologies – AWS - Sage maker, Tensorflow, Amazon ML Feature Store
• A results-oriented mind-set with strong analytical skills and problem-solving ability
• Strong interpersonal skills and ability to communicate clearly and effectively (orally and in writing)
• Strong organizational skills with ability to work on multiple projects
• Ability to design and maintain an end-to-end ML tool, from data injection to UI.
• Experience with Spark Experience with graph query languages such as Gremlin.