Key Skills: Pytorch, Boost, Sci-Kit Learn, Python, GCP, Vertex AI (Google), Tensorflow, LangChain Development, DevOps, Prometheus, Terraform, Gemini, Spark

Roles and Responsibilities:

• Design, build, train, evaluate, and deploy machine learning models using Vertex AI.
• Develop and optimize ML pipelines for preprocessing, feature engineering, model training, validation, and inference.
• Build cloud-native AI/ML solutions using GCP services and enterprise best practices.
• Develop scalable AI applications for NLP, Computer Vision, Recommendation Systems, Forecasting, and Generative AI use cases.
• Fine-tune and deploy foundation models and LLM applications using Vertex AI Studio and Gemini APIs.
• Implement CI/CD pipelines for ML deployment, monitoring, experiment tracking, and drift detection.
• Containerize ML applications using Docker and deploy them using Kubernetes/GKE.
• Build and support ETL/ELT pipelines for structured and unstructured datasets.
• Collaborate with business and engineering teams to translate requirements into AI-driven solutions.
• Contribute to architecture discussions, platform improvements, and operational excellence initiatives.

Skills Required:

• Strong experience with Vertex AI and Google Cloud Platform (GCP).
• Hands-on expertise in Python, TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Experience building and deploying scalable ML and AI applications.
• Knowledge of MLOps, CI/CD, model monitoring, and cloud-native deployment practices.
• Familiarity with LangChain, Gemini APIs, and Generative AI/LLM applications.
• Experience with Docker, Kubernetes/GKE, Terraform, and Spark is an added advantage.
• Understanding of monitoring and observability tools such as Prometheus and Cloud Monitoring.
• Strong analytical, problem-solving, and collaboration skills.

Education: Bachelor's degree or Master's degree in Computer Science or a related field.