Role : Machine Learning Engineer (Part-Time | Flexible Remote)

Are you an experienced ML Engineer looking for a flexible, part-time opportunity to work on real-world impact projects? Join us in building intelligent systems that match candidates to projects using structured skills, assessments, and feedback data.

This is your chance to own end-to-end ML pipelines and work on meaningful automation in the HRTech space all on your own schedule.

Role Overview :

Were looking for an ML Engineer to architect and deploy predictive models that power candidate project matching intelligence, leveraging structured applicant data.

You'll design scalable ML workflows and inference pipelines on AWS.

Key Responsibilities :

- Build data pipelines to ingest & preprocess applicant data (skills, assessments, feedback).

- Engineer task-specific features and transformation logic.

- Train predictive models (logistic regression, XGBoost, etc.) on SageMaker.

- Automate batch ETL, retraining flows, and storage with AWS S3.

- Deploy inference endpoints with Lambda, and integrate with systems in production.

- Monitor model drift, performance, and feedback loops for continuous learning.

- Document architecture, workflows, and ensure explainability.

Youll Need :

- 4 to 6+ years in ML/AI engineering roles.

- Strong command of Python, scikit-learn, XGBoost, and feature engineering.

- Proven experience with AWS ML stack: SageMaker, Lambda, S3.

- Hands-on SQL/NoSQL and automated data workflows.

- Familiarity with CI/CD for ML (CodePipeline, CodeBuild, etc.

- Ability to independently own schema, features, and model delivery.

- Bachelors or Masters in CS, Engineering, or related field.

Bonus Points For :

- NLP experience (extracting features from feedback/comments).

- Familiarity with serverless architectures.

- Background in recruitment, talent platforms, or skills-matching system.