Machine Learning Engineer

HCLTech

India · Full-time · Mid-Senior

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.

Key Skills

Ranked by relevance

cloud machine learning tensorflow python keras spark cicd ai google cloud platform data visualization containerization deep learning prototypes pytorch docker pandas numpy gcp

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