- Location
- IN-Bangalore (ITPB), India
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Experience
- 5+ years
- Source
- Workday
Description
TransPerfect Is More Than Just a Job…
Our greatest asset is our people, and nothing is more important to us than ensuring that everyone knows that. Each of our 100+ offices has its own individual identity, and each also has its own unique rewards.
Contract: 12 months (with potential extension based on performance)
Client: A leading multinational telecommunications company – Motorola
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.
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.
Tech Stack
- 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)