AI/ML Engineer

About the Role

We are seeking a Senior AI/ML Engineer to join our clients AI team and contribute to the development of cutting-edge intelligent systems. In this role, youll be responsible for designing, training, and deploying machine learning models that power innovative features across devices and services. This is an exciting opportunity to work with a global technology leader in the telecommunications sector, applying your expertise to real-world applications that impact millions of users.

Contract: 12 months (with potential 12-month extension based on performance)

Client: A leading multinational telecommunications company (name disclosed at interview)

We are seeking a Senior AI/ML Engineer to join our clients AI team and contribute to the development of cutting-edge intelligent systems. In this role, youll be responsible for designing, training, and deploying machine learning models that power innovative features across devices and services. This is an exciting opportunity to work with a global technology leader in the telecommunications sector, applying your expertise to real-world applications that impact millions of users.

Responsibilities

• Build, train, and maintain systems based on machine learning and natural language processing.
• Write machine learning algorithms based on transformers.
• Conduct experiments, evaluate model performance, and suggest improvements.
• Prepare quick scripts to automate tasks such as metric generation, file conversions, etc., when necessary.
• Lead and collaborate with engineers to implement production-ready AI solutions.

Requirements

• Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field.
• 5-7 years of experience in developing and deploying machine learning models.
• At least 3 years of experience leading a team.
• Experience with deep learning and statistical learning in Natural Language Processing (NLP).
• Familiarity with text processing libraries.
• Hands-on experience with LLM models, prompting libraries, and fine-tuning.

Programming & ML Frameworks

• Python
• TensorFlow
• PyTorch
• scikit-learn
• XGBoost / LightGBM (common in practical ML pipelines)
• NumPy, Pandas
• Jupyter Notebooks
• Data preprocessing & feature engineering tools
• Version Control & CI/CD
• Git / GitLab
• Jenkins

Development & Experimentation Tools

• TensorBoard
• Weights & Biases (W&B) or Neptune.ai
• ONNX (optional but common for deployment/optimization)

Other Useful Skills (common for telecom/edge AI)

• Experience with edge AI model optimization (TensorRT, CoreML, TFLite)