Data Analysis & Simulation Professional (Gen AI Engineer)

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.