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 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.