ML Engineer (Forecasting) | NDA

AI/ML Engineer

GT was founded in 2019 by a former Apple, Nest, and Google executive. GT's mission is to connect the world's best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands. Our clients operate in industries like healthcare, life sciences, fintech, retail, e-commerce, finance and many more - giving our team exposure to real-world, high-impact projects.

We're looking for an AI/ML Engineer to join a UK-based client in the healthcare and pharmacy domain.

The role focuses on forecasting and time-series modeling, developing solutions that directly improve operational efficiency.

Project duration: 12 weeks (with possible extension).

Start date: June 15 (flexible - part-time start possible).

Project Details: The project focuses on developing a forecasting solution for a large healthcare network. It uses historical clinic and marketing data to predict clinic usage and staffing needs, helping optimize scheduling and resource allocation. The goal is to build a scalable, data-driven platform that improves operational efficiency.

Responsibilities:

• Design, train, and deploy ML models for time-series forecasting and related data tasks
• Build and maintain data pipelines using cloud-native tools (AWS, GCP, or Azure)
• Develop and optimize forecasting models (Prophet, ARIMA, LSTM, TimeGPT)
• Collaborate with data, product, and cloud engineers to deliver reliable, scalable solutions
• Participate in different stages of the project lifecycle - from discovery and PoC to production deployment, presenting your work to stakeholders

Essential knowledge, skills & experience (must-have):

• 4+ years of experience in Machine Learning / Data Science
• Proven experience with forecasting / time-series modeling (Prophet, ARIMA/SARIMA, LSTM, TimeGPT, XGBoost or similar)
• Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch)
• Experience with model deployment and production ML systems
• Familiarity with data preprocessing and feature engineering for time-series data
• Familiarity with cloud environments (Azure, AWS, or GCP)
• Version control (Git) and SQL
• Advanced English level

Nice-to-have:

• Experience with Generative AI / LLMs
• Experience with RAG pipelines
• Experience with vector databases (Weaviate, Milvus)
• Familiarity with LLM evaluation frameworks (e.g. DeepEval)

Soft Skills:

• Strong sense of ownership and accountability
• Proactive attitude and ability to work independently
• Clear and confident communication with both tech and non-tech stakeholders
• Comfortable working in ambiguity and helping define requirements
• Strategic thinking and focus on business impact
• Team player

Interview Steps:

1. GT interview with Recruiter
2. Technical interview
3. Final interview