onsite
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer will design and implement AI/ML solutions for complex business problems, developing and optimizing models using frameworks like TensorFlow and PyTorch. This role involves building scalable data pipelines with Python and Spark, and deploying and monitoring models on public cloud platforms such as AWS, Azure, and GCP.
About the role
About the Role
The role involves designing and implementing AI/ML solutions to address complex business challenges, developing and optimizing machine learning models, and ensuring seamless deployment and monitoring of these models on public cloud platforms.
Responsibilities
- Design and implement AI/ML solutions for complex business problems across domains like Banking, Finance, Telecom, Retail, and Technology.
- Develop and optimize Machine Learning and Deep Learning models using frameworks such as TensorFlow, Keras, PyTorch.
- Build and maintain scalable data pipelines and ML workflows using Python, Spark, PySpark, and GCP services.
- Deploy and monitor models on public cloud platforms (AWS/Azure/GCP), with hands-on experience in Vertex AI, BigQuery, Cloud Composer.
- Transform prototypes into robust, production-ready solutions.
- Implement CI/CD principles for ML Ops, ensuring seamless integration and deployment.
- Work with containerization and orchestration tools like Docker and Kubernetes.
- Apply advanced statistical analysis, text mining, and machine log processing techniques.
- Collaborate with cross-functional teams to ensure alignment with business goals.
Required Skills
- AI/ML Development: TensorFlow, Keras, PyTorch, Scikit-learn, Spark ML.
- Programming: Python (expert), R (working knowledge), Spark.
- Data Handling: Pandas, NumPy, PySpark.
- Cloud Platforms: Google Cloud Platform (Vertex AI, BigQuery, AI/ML services), AWS/Azure experience is a plus.
- ML Ops: CI/CD pipelines, model deployment, monitoring.
- Statistical Analysis: Strong foundation in statistics and data-driven decision-making.
- Domain Knowledge: Exposure to supervised, unsupervised, and reinforcement learning.
Preferred Skills
- Experience with additional programming languages.
- Familiarity with other cloud platforms.
- Knowledge of data visualization tools.