Machine Learning Engineer

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 Tensor Flow, 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, Big Query, 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:
Tensor Flow, Keras, PyTorch, Scikit-learn, Spark ML.
- Programming:
Python (expert), R (working knowledge), Spark.
- Data Handling:
Pandas, Num Py, PySpark.
- Cloud Platforms:
Google Cloud Platform (Vertex AI, Big Query, 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.