GenAI engineer

• 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.

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Key Responsibilities:

1. Data Pipeline Development:

o Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.

2. Generative AI Integration:

o Collaborate with data scientists to integrate generative AI models into the log analysis workflow.

o Develop APIs and services to deploy AI models for real-time log analysis and insights generation.

3. Data Monitoring and Maintenance:

o Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.

o Troubleshoot and resolve issues related to data ingestion, processing, and storage.

4. Collaboration and Documentation:

o Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.

o Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.

 Evaluation and Testing: 

o Conduct thorough testing and validation of generative models

 Snowflake Utilization: 

o Design and optimize data storage and retrieval strategies using Snowflake.

o Implement data modeling, partitioning, and indexing strategies to enhance query performance.

 Research and Innovation: 

o Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities. 

o Experiment with different architectures and approaches like Agentic AI