Senior ML Engineer - Time Series & Forecasting

Senior ML Engineer - Time Series and Forecasting - Abu Dhabi 🇦🇪

A key partner of mine, a fast-growing start-up, is looking to hire a Senior ML Engineer to join and lead their ML efforts.

We are looking for a highly skilled Senior Machine Learning Engineer to develop and deploy production-ready ML models at scale. This role will focus on time series modeling, anomaly detection, and forecasting systems, working with vast amounts of structured and unstructured data. The ideal candidate should have strong expertise in building end-to-end ML pipelines, from data engineering and feature extraction to deploying models in production.

This a great opportunity to join at the early stages in a well-funded start-up, and have a genuine impact on the Proptech industry.

Stack:

PyTorch, TensorFlow, NumPy, Pandas, MLflow, Kubernetes, AWS, Scikit-learn

Key Responsibilities

• Design and implement robust time series forecasting models, anomaly detection systems, and predictive analytics solutions that run in real-time environments.
• End-to-End ML Pipeline Ownership – Handle everything from data cleaning, feature engineering, model training, evaluation, and deployment in a scalable and efficient manner.
• Production-Ready Code – Write high-quality, optimized, and well-documented code that adheres to software engineering best practices.
• Scalability & Deployment – Deploy ML models using Kubernetes, Docker, and AWS for real-time inference and batch processing.
• Collaboration – Work closely with data engineers, software developers, and product teams to ensure seamless integration of ML models into production systems.
• Model Monitoring & Maintenance – Implement continuous monitoring, retraining pipelines, and model performance tracking to ensure high accuracy and reliability.

Key Qualifications

• Expert in Time Series Modeling – Proven experience with LSTMs, Transformers, Bayesian models, or deep learning-based forecasting techniques.
• Anomaly Detection Expertise – Hands-on experience implementing Isolation Forests, Autoencoders, LOF, statistical anomaly detection methods.
• Strong Programming Skills – Proficiency in Python, PyTorch, TensorFlow, Scikit-learn, XGBoost, Statsmodels.
• Data Engineering Skills – Experience with Spark, SQL, Pandas, and cloud-based data warehouses (BigQuery, Snowflake, Redshift).
• Production ML Deployment – Strong understanding of MLOps, containerization (Docker, Kubernetes), CI/CD pipelines, and cloud deployment.
• Big Data & Streaming – Familiarity with Kafka, Apache Flink, or real-time data processing tools is a plus.
• Strong Problem-Solving & Optimization Skills – Ability to handle large-scale datasets efficiently and optimize model performance.