Machine Learning Engineer

Compunnel, Inc.; Columbus (GA); On-site; USD 90,000 - 120,000; Full time

Job summary

Compunnel, Inc. Lead I role focuses on designing and developing ML solutions to enable intelligent experiences. You will collaborate with business, technology, and product teams to understand objectives and formulate ML problems with minimal guidance from Lead II.

The position emphasizes data wrangling, ML experiments, model tuning, and deployment support in a Columbus, Georgia context.

Qualifications

• Design and own ML solutions addressing business problems.
• Perform data wrangling to prepare datasets.
• Run ML experiments and baseline models to assess feasibility.
• Collaborate with product and business teams to define success metrics.

Responsibilities

• Execute data wrangling activities related to the problem.
• Conduct ML experiments to build baseline models.
• Fine-tune models for improved performance.
• Test models against business acceptance criteria.
• Document artefacts for business communication.
• Collaborate with data scientists to deploy models.
• Collaborate with product teams on new releases.
• Set OKRs and track progress for self and team.
• Identify metrics to validate models and report to product teams.
• Monitor trends and prototype feasibility for integration.

Skills

ML model development

Data wrangling

Experimentation

Model deployment

Python

TensorFlow

scikit-learn

Tools

Python

TensorFlow

scikit-learn

Job description

Lead I

Design and develop ML solutions, that will enable intelligent experiences and provide value. Collaboratively work with business, technology, and product teams to understand the product objectives and formulate the ML problem, under minimal guidance from Lead II

Key Responsibilities

1. Executes relevant data wrangling activities related to the problem
2. Conduct ML experiments to understand feasibility; building baseline models to solve the business problem
3. Fine tune the baseline model for optimum performance
4. Test Models internally per acceptance criteria from the business
5. Identify areas and techniques to optimize the model based on test results
6. Document relevant artefacts for communicating with the business
7. Work with data scientists to deploy the models.
8. Work with product teams in planning and execution of new product releases.
9. Set OKRs and success steps for self/ team and provide feedback of goals to team members
10. Identify metrics for validating the models and communicate the same in business terms to the product teams.
11. Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions