Job description
- We are seeking for a 5'7-year experience AI engineer with a strong background in machine learning, programming skills, and a deep understanding of generative models. The position is responsible for turning research into practical solutions that address real-world problems while ensuring the reliability and ethical use of generative AI in their applications.
Technical Requirements:
- Strong proficiency in Python for data processing and automation.
- Handson experience with generative AI models and their integration into data workflows.
- Handson experience with prompt engineering and LLM models (Opensource and Closesource)
- Handson experience with Application development framework like LangChain, LangGraph etc.
- Familiarity working with REST frameworks like Fast API, Angular, Flask and DJango.
- Experience with cloud platforms (AWS, GCP, Azure) and related services is a plus.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
- As a Data Analysis & Simulation Professional, the person will be responsible for:
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.
Evaluation and Testing:
- Conduct thorough testing and validation of generative models.
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.
- Snowflake Utilization(Good to have)
- Design and optimize data storage and retrieval strategies using Snowflake.
- Implement data modeling, partitioning, and indexing strategies to enhance query performance.
