ML Ops Engineer

Huntington Bank Qual Notes:

AWS cloud certification

Docker, Sagemaker, Container

Hadoop Cluster for Datalake and production runs of models

This is done via batch - not real time

Weekly, daily, monthly updates to the data - model runs and usage daily

Migrating away from Hadoop to AWS - see it in 2 ways - data lake aspect (well handled by current team) - data science piece (MLOps is where they are needing help)

MLOps is on the horizon, but more development needs to be done - Containers, Terraform, CloudWatch

Needing someone with a strong drive to learn in the MLOps space - open to a mid-level candidate with the hunger to learn more

Ultimately a strong AWS engineer with a focus in the below areas Must Haves:

CloudWatch

Sagemaker

Python

Containers - Docker and Kubernetes Search String:

Aws AND sagemaker AND python AND (container* OR kubernetes OR docker) Screener:

Responsible for / Working On:

Had a big cluster of CPUs stored a bunch of data, served as place where data scientists would build their models and put into PROD

Wasn't scalable

May be getting dtaa from some unique place, have a favorite software, or even favorite sub packages within certain software, and all this uniqueness leads to a lot of inefficiency because nobody is doing the same thing... even if Bill builds something great with code, it cant be used by Susan because any one of the above reasons.

Need to take away approach where they are building model 1, deploy it, build model 2 deploy it, etc.

This plays into what is called MLOps

Instagram, example of how various parts of the app are actually different "containers" behind the scene that are then clustered out based on the volume of people trying to access different features of the app at different times.

To run models may require a large number of containers that essentially feed and deploy that back to the end users.

Meta, LinkedIn, large tech companies are well ahead of banks and finTechs.

Hadoop data model and storage

need to focus n making the models. About how to make all the models efficiently in order to run all at the same time.

Key need currently is focused on AWS, and having key understanding of containers (Docker-this is key), Sagemaker (AWS analytics modules).

MLOps - need to come from this background.

Marketing example, this person has $123k will send them a targeted message to open a savings account and the bank will give them $500

Model monitoring is also crucial to ensure what is running is actually effective, if not, what do we need to change... then redeploy.

LinkedIn, different models would be things like DMs, jobs, various different apps within an app.

Required Skills : Excel, aws, python, cloud

Basic Qualification :

Additional Skills :

Background Check :Yes

Drug Screen :Yes

Notes :

Selling points for candidate :

Project Verification Info :

Candidate must be your W2 Employee :Yes

Exclusive to Apex :No

Face to face interview required :No

Candidate must be local :Yes

Candidate must be authorized to work without sponsorship ::No

Interview times set : :No

Type of project :Development/Engineering

Master Job Title :Cloud: Virtualization Engineer

Branch Code :Minneapolis