AiOps Engineer

4 hours ago

Bangalore Urban, India L&T Technology Services Full time

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

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