Ai/Ml Engineer

About the Role We are seeking a Senior AI/ML Engineer to join our client’s AI team and contribute to the development of cutting-edge intelligent systems. In this role, you’ll 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) Responsibilities Develop and fine-tune machine learning algorithms. Conduct experiments, evaluate model performance, and suggest improvements. Lead and collaborate with engineers to implement production-ready AI solutions. Document processes and results for scalability Requirements Bachelor’s or Master’s 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. Proficiency in Python and popular ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Strong understanding of data preprocessing, model evaluation, and hyperparameter tuning. Experience with SQL, Pandas, NumPy, and working with structured and unstructured data. Familiarity with MLOps tools (Docker, Kubernetes, MLflow, Airflow) is a plus. Experience with cloud platforms (AWS, Azure, or GCP) preferred. Programming & ML Frameworks Python TensorFlow PyTorch scikit-learn XGBoost / LightGBM (common in practical ML pipelines) Data & Analytics NumPy, Pandas SQL Jupyter Notebooks Data preprocessing & feature engineering tools Cloud / Infrastructure AWS (S3, SageMaker, Lambda, ECS) Azure (ML Studio, Blob Storage) GCP (AI Platform, BigQuery) (Any of the three—whichever the client prefers) Version Control & CI/CD Git / GitHub / GitLab Jenkins, GitHub Actions, or GitLab CI 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)