We are seeking a Mid-Level 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.
Required Qualifications
Develop and fine-tune machine learning algorithms.
Conduct experiments, evaluate model performance, and suggest improvements.
Collaborate with senior engineers to implement production-ready AI solutions.
Document processes and results for scalability.
Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.
2–5 years of experience in developing and deploying machine learning models.
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) In
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)