Principal Machine Learning Engineer

** Job Description*
* Implements machine learning (ML) models for production. Ensures the readiness of machine learning models for deployment in production. Automates machine learning workflows. Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment. Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Addresses issues in machine learning infrastructure and workflows.

Collaborates with stakeholders to integrate machine learning models into new or extant systems. Develops, maintains, and refines tools, platforms, and services for internal use. Develops efficient, bug-free code from scratch. Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.

** Responsibilities*
* ** Key Responsibilities*
* ** Machine Learning and Data Modeling - Model Productionization:*
*
- Utilizes

machine learning (ML) and software development knowledge to implement ML models

for production.

- Engages

in transforming machine learning prototypes into production-ready models.

- Collaborates

with multiple stakeholders, such as Development Leads, Product Management,

Operations, and Release Management, to make, adopt, and communicate technical

decisions, and shape the development and delivery of software.

** Model Development and Deployment - Model Deployment:*
*
- Ensures

ML model readiness for deployment by scaling models, cleaning model code, and

ensuring production quality standards are met.

- Automates

machine learning workflows, from data extraction, transformation, and loading

(ETL) to model deployment and monitoring, to establish the continuous

integration and continuous delivery of machine learning solutions.

** Model Development and Deployment - Model Performance:*
*
- Creates

infrastructure and frameworks to monitor the performance and alignment with

design criteria of trained models and/or systems.

- Proactively

monitors the performance of deployed models and troubleshoots independently or

in collaboration with Data Science.

- Develops

novel metrics that provide analytical insights to non-technical stakeholders on

how well machine learning models are operating.

** Model Development and Deployment - Data Quality:*
*
- Evaluates

potential issues related to data quality (e.g., bias, fairness), data security,

and data privacy, and minimizes their impacts on data analyses and modeling.

- Engages

in tasks such as data cleaning, preprocessing, and feature identification to

prepare for and enable model training.

** Internal Collaborations and Impacts - Model Integration and Operation:*
*
- Collaborates

with multiple stakeholders (e.g., data scientists, software developers) to

integrate ML models into new or existing systems.

- Maintains

the partnership between model development and operations, ensuring smooth

deployment and continuous improvement of ML models.

- Understands

operational considerations of model deployment (e.g., performance, scalability,

stability, maintenance).

- Provides

expert troubleshooting and debugging support, addresses issues in machine

learning infrastructure and workflow, and creates robust solutions to prevent

future problems.

** Internal Collaborations and Impacts - Tool Development:*
*
- Develops,

maintains, and refines tools, platforms, environments, and services for

internal use.

** Internal Collaborations and Impacts - Coding and Documentation:*
*
- Develops

efficient, bug-free, medium-complexity code from scratch, and properly

maintains and organizes the existing codebase.

- Implements

best practices for version control, code review, and code delivery/deployment.

- Builds

and maintains professional documentation for technical processes

(experimentation, data collection and analyses, model building).

- Tests

and reviews code for bugs.

** Machine Learning Expertise:*
*
- Maintains

familiarity with current developments in the machine learning field and

integrates knowledge into model development.

- Maintains

familiarity with the usage and development of third-party machine learning

frameworks, packages, and libraries (e.g., PyTorch, Tensor Flow, Keras) to

continuously evaluate their performance and scalability, and integrate them

into production environments.

** Core Responsibilities*
* ** Planning& Execution:*
*
- Manages

and coordinates moderately complex tasks, monitoring timelines and deliverables

to ensure timely completion and adherence to requirements for a moderately

sized project or initiative.

- Efficiently

delegates, monitors, and prioritizes work across multiple projects, providing

technical oversight and adjusting plans to address shifts in resources or

timelines.

** Collaboration& Partnership:*
*
- Collaborates

across the organization to align on expectations and achieve shared objectives.

- Leverages

understanding of business leaders, stakeholders, and/or customers to ensure

proposed solutions meet their needs.

- Supports

inclusivity by actively seeking and listening to…