The ML Engineer will be a core member of the Data Science team at Propelis, collaborating with a diverse group of data scientists, data engineers, and developers. The team works across machine learning (ML), MLOps, machine vision, NLP, IoT, and prompt engineering. The ML Engineer will focus on deploying scalable AI/ML solutions in production and driving automation.

Profile

The ML Engineer is responsible for building robust, production-ready models and maintaining scalable machine learning pipelines. This role requires strong technical depth in MLOps, model optimization, and monitoring while also working closely with business stakeholders to support decision-making with AI-powered insights.

Key Responsibilities

Design, develop, and maintain machine learning models to solve business challenges and drive automation.
Implement and optimize ML algorithms for efficiency, scalability, and actionable insights.
Conduct experiments, A/B testing, and model evaluations to improve model performance.
Develop, containerize, and deploy AI/ML systems in production environments using best practices.
Automate and streamline end-to-end ML pipelines, ensuring reliable transitions from development to production.
Monitor and troubleshoot model performance, accuracy, and drift in live environments.
Execute and automate validation tests to ensure robustness and reliability.
Optimize training and inference workflows to enhance speed and scalability.
Manage model versioning, deployment strategies, and rollback mechanisms.
Collaborate with product, analytics, and engineering teams to align ML solutions with business goals.
Contribute to team codebases, documentation, and internal knowledge sharing.

Skills & Experience

Strong background in machine learning, statistics, and MLOps practices.
Experience with machine learning algorithms such as classification, XGBoost, decision trees, and deep learning.
Proficient in Python and familiar with libraries such as pandas, scikit-learn, NumPy, and PyTorch.
Knowledge of data pipelining and streaming tools.
Familiarity with SQL, NoSQL, or Cassandra is a plus.
Experience deploying ML models on cloud platforms (Azure, AWS, or GCP).
Strong communication, problem-solving skills, and ability to work in cross-functional teams.
Interest or experience in NLP, prompt engineering, or generative AI (e.g., GPT) is a plus.

Education Requirements

Bachelor’s degree in Data Science, Computer Science, Applied Mathematics, or related field.
Master’s degree preferred.

We are committed to ensuring equal opportunity in all aspects of employment, including recruitment. We encourage applications from all qualified individuals, particularly those who may contribute to the further diversification of our organization. If you require any form of accommodation during the recruitment process, please do not hesitate to inform us. Together, we strive to foster an environment where everyone can thrive and be their authentic selves.