Only immediate to 15 days joiner
Experience 8 to 10 yrs
Key Responsibilities:
- Develop and Deploy AI Solutions: Design, build, and deploy end-to-end Machine Learning and Generative AI pipelines on Google Cloud Platform, using Vertex AI services such as Vertex Pipelines, Model Registry, and Endpoints.
- Implement Generative AI Applications: Focus on implementing and optimizing solutions using Open source Large Language Models (LLMs) for tasks like code generation, text summarization, content creation, and intelligent agent development.
- MLOps and Productization: Automate collection and visualization of data, model, and operational metrics. Implement and manage MLOps pipelines to automate model deployment, monitoring, and maintenance. Deploy models in scalable production environments using GCP.
- Leverage Google Cloud Services: Work extensively with GCP services beyond Vertex AI, including BigQuery for data warehousing, Cloud Storage for data management, and Cloud Functions for serverless compute.
- Build Retrieval-Augmented Generation (RAG) Systems: Design and implement RAG-based systems by integrating LLMs with external APIs, vector databases and private knowledge sources to enhance model grounding and accuracy.
- Model Optimization and Performance Tuning: Optimize model serving performance, cost, and throughput for both real-time and batch predictions.
- Cross-Functional Collaboration: Partner with Data Scientists, Data Engineers, and product teams to translate business requirements into scalable, production-ready AI solutions.
- Stay Ahead of the Curve: Continuously research and experiment with the latest advancements in AI, ML, and Generative AI, applying new techniques to solve complex business problems.
- Proficiency in Python and one other languages Java, Go, C/C++, R, SQL
- 5+ years of hands-on experience in AI/ML engineering, building and deploying machine learning models in production environments.
- Tools: Linux, git, Jupyter, IDE, ML frameworks: Tensorflow, Pytorch, Keras, Scikit-learn, Kubeflow, MLflow
