Job Description

Position Overview

We are looking for an experienced Machine Learning Engineer to join our growing efforts in machine learning and generative AI. You will be part of our Data Science Team, working alongside Data Scientists, ML Engineers, Knowledge Experts, and Developers to build and maintain machine learning features in production. Additionally, you will contribute to developing the infrastructure and tooling necessary for model development.

Job Responsibilities

in building and integrating production features that utilize machine learning and generative AI technologies, working closely with the Data Scientists, ML Engineers, Knowledge Experts and Engineering teams

Participate in the development and optimization of infrastructure and processes for machine learning prototyping, deployment, and iterative improvements based on customer feedback

Support the development and maintenance of our AI platform, aiding in both standard and custom AI model solutions for clients

Architect robust data pipelines, addressing key issues of scalability and performance. Provide expert recommendations on their usage and advocate for best practice patterns within the team

Engage in project initiatives and contribute to the planning process, ensuring alignment with project goals and team objectives

Play a role in educating internal stakeholders about our machine learning projects and their business implications

Other duties as assigned

A little bit about you...

3-5 or more years of experience in Machine Learning Engineering role

Fluency in Python and the associated Data Science stack (numpy, scipy, scikit-learn, pandas)

Fluency in at least one relational database language

You can communicate clearly and empathetically with developers, product managers, and UX designers to explain the abilities and limitations of ML systems

Experience with deep learning and genAI models, both third party and open source preferred

Expertise in designing and implementing robust data pipelines, particularly where scalability and performance are critical

Ability to architect and deploy basic machine learning models in a production environment

Experience with a cloud-based infrastructure environment (AWS, GCP, etc.) and containerization technology (docker, Kubernetes)

Knowledge of machine learning model optimization, including the ability to advise on trade-offs and options

Familiarity with model drift, and the ability to monitor and respond appropriately in straightforward scenarios

Familiarity with at least one CI/CD tool and experience in modern principles of software development and version control

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