Location: Singapore, Singapore
Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we’re driven by a mission to build a future we can all trust.
In Singapore, Thales has been a trusted partner since 1973, originally focused on aerospace activities in the Asia-Pacific region. With 2,000 employees across three local sites, we deliver cutting-edge solutions across aerospace (including air traffic management), defence and security, and digital identity and cybersecurity sectors. Together, we’re shaping the future by enabling customers to make pivotal decisions that safeguard communities and power progress.
Responsibilities:
- Design and conduct exploratory data analysis to identify new ideas, hidden patterns, and opportunities for air traffic optimization.
- As opposed to other domains, the quantity of data in the aeronautical space is not huge, therefore thinking outside of the traditional ML boxes is essential to propose new AI solutions specific to that field.
- Design, develop, and deploy ML models for real-time classification, regression, and sequence prediction using PyTorch, TensorFlow, or Scikit-learn, including transformer-based architectures for spatial-temporal problems
- Develop reinforcement learning agents using algorithms such as DQN, PPO, and Actor-Critic, and apply them to simulated and real-world environments via OpenAI Gym or custom setups.
- Build and optimize RAG pipelines grounded on domain-specific documentation to support AI-generated reasoning and recommendations
- Evaluate LLM outputs for hallucination and groundedness, and develop domain-specific benchmarks to assess LLM reasoning in ATM contexts
- Build and automate machine learning pipelines using tools like Kubeflow, Airflow, or similar orchestration frameworks.
- Design reproducible workflows for data preprocessing, training, evaluation, and deployment.
- Integrate ML models into scalable APIs and deploy them to cloud-native environments using Docker and Kubernetes.
- Monitor model performance over time, retrain, and iterate as needed based on live data and production drift.
- Maintain experiment tracking, model versioning, and reproducibility using tools like MLflow or Weights & Biases.
- Collaborate with DevOps and backend engineers to ensure seamless integration of ML components into larger systems.
Requirements:
Education
- Bachelors in Computer Science or Information Technology
- Masters degree in Computer Science or Data Science, if applicable
Essential Skills/Experience
- Domain & Data
- Aeronautical domain knowledge would be a major plus. At minimum, some experience in a domain that is not one of the common ones (vision, chatbots...).
- Experience in a domain where available historical data is not huge would be a plus.
- Good understanding of data (statistics, features, analytics) and how to map them to the domain.
- High level of core mathematic skills: algorithms (in particular for prediction), optimizations.
- Core ML
- 3-5 yrs delivering ML projects end-to-end (data prep to production).
- Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
- Familiarity with modern deep learning architectures — including transformers, attention mechanisms, or encoder-decoder models.
- LLM & RAG
- Experience building and optimizing RAG pipelines, including chunking strategies, retrieval tuning, and context management
- Proficiency in prompt engineering and agentic frameworks such as LangChain or LlamaIndex
- Practical experience evaluating LLM outputs — including hallucination detection, and groundedness against source material
- Experience designing domain-specific benchmarks to evaluate LLM reasoning and knowledge in specialized fields
- Reinforcement Learning
- Solid understanding of reinforcement learning theory and experience with at least one RL algorithm (e.g. PPO, DQN).
- Practical experience with OpenAI Gym, Gymnasium, or equivalent RL environments.
- MLOps & Engineering
- Strong grasp of MLOps practices, including the use of Kubeflow Pipelines, MLflow, and model serving platforms.
- Experience deploying models in containerized environments using Docker and Kubernetes.
- Hands-on with ETL/ELT tooling (Apache Spark) and modern data-warehouse/lake (S3-based).
- Knowledge of CI/CD principles for machine learning workflows and model promotion strategies.
- Ability to build and debug data pipelines that support both training and inference workloads.
Desirable Skills/Experience
- Working knowledge of other languages (e.g., Python3, Scala2 or Scala3, Go, TypeScript, C, C++17, Java17)
- Familiar with designing and/or implementing AI/MLOps pipelines in public cloud (e.g., Azure, AWS, GCP)
Essential / Desirable Traits
- Possess learning agility, flexibility and pro-activity
- Comfortable with agile teamwork and user engagement
At Thales, we’re committed to fostering a workplace where respect, trust, collaboration, and passion drive everything we do. Here, you’ll feel empowered to bring your best self, thrive in a supportive culture, and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference – for a safer, greener, and more inclusive world.