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.
