Data Scientist / ML Engineer - Theseus Group

About Us: Theseus Group specializes in automating and enhancing business processes using
cutting-edge AI and software solutions. Our mission is to empower businesses by delivering
faster progress with fewer human dependencies, enabling them to streamline operations and
solve complex problems at scale.

Job Overview
We are seeking a highly skilled Machine Learning Engineer to lead the deployment of
large-scale ML solutions for real-world applications. Initially, you will focus on deploying, scaling,
and maintaining ML models accessible via APIs to deliver robust, efficient solutions to complex
business problems. Over time, the role will evolve to involve more in-house model building,
fine-tuning, and optimization. You will be responsible for ensuring model accuracy, performance,
and scalability across a diverse set of use cases.

Key Responsibilities
- Deploy complex ML solutions at scale, ensuring seamless integration and model
performance through APIs.
- Design, build, and optimize ML pipelines for data preprocessing, feature engineering,
model training, and evaluation.
- Ensure robust testing of models in diverse environments to validate accuracy, reliability,
and scalability across various use cases.
- Implement and maintain CI/CD pipelines specifically for ML models, automating training,
deployment, and updates using AWS services (SageMaker, Lambda, ECS).
- Continuously monitor and refine deployed models to optimize performance, troubleshoot
issues, and ensure seamless scalability.
- Collaborate closely with data engineers, product teams, and back-end developers to
integrate ML models into production systems.
- Develop and apply advanced techniques for model testing, evaluation, and fine-tuning
based on specific client needs and performance metrics.
- Eventually, lead efforts to build, train, and fine-tune custom machine learning models
in-house.

Required Skills
- 3+ years of experience deploying and scaling machine learning models in production
environments, particularly using cloud platforms like AWS.
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, and Scikit-learn.
- Strong experience with AWS services including SageMaker, Lambda, ECS, RDS, and S3.
- Expertise in designing scalable ML architectures and pipelines for efficient model
deployment.
- A strong background in testing model effectiveness across diverse scenarios, refining
models for performance and reliability.
- Familiarity with API-based ML models and experience in integrating third-party models
into larger systems.
- Experience with AWS CDK, CloudFormation, and infrastructure as code for automating
model deployment.
- Experience with data preprocessing, feature engineering, and hyperparameter tuning for
a variety of model types (NLP, classification, regression, etc.).
- Proficiency in tools for data visualization, model explainability, and debugging (e.g.,
SHAP, Dash).

Preferred Skills
- Familiarity with MLOps practices for managing models post-deployment.
- Strong understanding of distributed systems and parallel computing for model training
and inference.
- Experience with AWS services such as SageMaker, Lambda, ECS, and RDS.
- Knowledge of modern model architectures like transformers, GPT models, or generative
AI.
- Familiarity with advanced ML techniques like transfer learning, reinforcement learning,
and transformer models
- Familiarity with MLOps best practices for monitoring, updating, and maintaining models
post-deployment.