Description
We are seeking a Generative AI Engineer with 2-5 years of experience to join our Digital Architecture team. The ideal candidate will have a strong background in developing agentic architectures and experience with various frameworks and observability tools relevant to generative AI applications. This role demands proficiency in Python, deployment experience, and familiarity with Retrieval-Augmented Generation (RAG) applications.
Technical skills requirement
Strong proficiency in Python for data processing and automation. Ability to write efficient and well-structured code.
Experienced with generative AI models and their integration into data workflows.
Experienced with prompt engineering and LLM models (Opensource and licensed. Ex Llama, OpenAI)
Experienced with Application development framework like LangChain or similar frameworks.
Experienced working with REST frameworks like Fast API, Flask and Django.
Good understanding of machine learning workflows and deployment.
Knowledge of ETL processes, data modeling, and data warehousing principles.
Familiarity with containerization and orchestration tools (Docker, Kubernetes).
Familiarity with Snowflake for data warehousing and analytics.
Experienced with cloud platforms (AWS, GCP, Azure) and related services is a plus.
Strong communication and collaboration skills.
Excellent problem-solving skills and attention to detail.
Key Responsibilities:
Data Pipeline Development:
Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.
Generative AI Integration:
Collaborate with data scientists to integrate generative AI models into the log analysis workflow.
Develop APIs and services to deploy AI models for real-time log analysis and insights generation.
Data Monitoring and Maintenance:
Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.
Troubleshoot and resolve issues related to data ingestion, processing, and storage.
Collaboration and Documentation:
Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.
Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.
5. Evaluation and Testing:
Conduct thorough testing and validation of generative models
6. Snowflake Utilization:
Design and optimize data storage and retrieval strategies using Snowflake.
Implement data modeling, partitioning, and indexing strategies to enhance query performance.
7. Research and Innovation:
Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities.
Experiment with different architectures and approaches.
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