Lead Gen-AI Engineer

Asks for

• CI/CDThe ad says“● Software Development Best Practices: Employ CI/CD, test-driven development, and sound architectural principles in all GenAI solutions.”
• data extractionThe ad says“● Data Extraction: Extract data from various sources (e.g., PDF files) as part of the RAG process.”
• ETLThe ad says“● ETL & Traditional Data Management: Build data pipelines to manage and integrate traditional relational data where necessary.”
• LLMsThe ad says“● As a Gen AI Engineer, you will be responsible for designing, testing, and deploying GenAI solutions that leverage LLMs and related technologies. You’ll work on solutions that incorporate prompt engineering, model evaluation, retrieval-augmented generation (RAG), and continuous performance monitoring, all while adhering to best practices in software engineering. This role blends data science, data engineering, and MLOps expertise, with a focus on making AI solutions reliable and production-ready.”
• load testingThe ad says“● Evaluation Scripts & Load Testing: Develop tests for the random nature of LLM outputs and conduct load testing to ensure system robustness, even when reliant on third-party APIs.”
• model evaluationThe ad says“● As a Gen AI Engineer, you will be responsible for designing, testing, and deploying GenAI solutions that leverage LLMs and related technologies. You’ll work on solutions that incorporate prompt engineering, model evaluation, retrieval-augmented generation (RAG), and continuous performance monitoring, all while adhering to best practices in software engineering. This role blends data science, data engineering, and MLOps expertise, with a focus on making AI solutions reliable and production-ready.”
• performance monitoringThe ad says“● Performance Monitoring: Build pipelines to monitor model responses and track performance metrics in production for ongoing optimization.”
• prompt engineeringThe ad says“● Prompt Engineering: Write, test, and refine prompts, including creating effective prompt chains and managing prompt versions for optimal results.”
• RAG architectureThe ad says“● RAG Architecture Implementation: Design and maintain retrieval-augmented generation (RAG) architectures. Work with data pipelines, vector databases, and search algorithms. Manage document chunking and embedding selection for optimal context window usage.”
• retrieval-augmented generationThe ad says“● As a Gen AI Engineer, you will be responsible for designing, testing, and deploying GenAI solutions that leverage LLMs and related technologies. You’ll work on solutions that incorporate prompt engineering, model evaluation, retrieval-augmented generation (RAG), and continuous performance monitoring, all while adhering to best practices in software engineering. This role blends data science, data engineering, and MLOps expertise, with a focus on making AI solutions reliable and production-ready.”
• solution prototypingThe ad says“● Solution Prototyping & Testing: Rapidly test various models and approaches to determine the best fit for the project without extensive over-investment. Stay updated on LLM advancements (e.g., GPT-3.5 to GPT-4).”
• test-driven developmentThe ad says“● Software Development Best Practices: Employ CI/CD, test-driven development, and sound architectural principles in all GenAI solutions.”
• vector databasesThe ad says“● RAG Architecture Implementation: Design and maintain retrieval-augmented generation (RAG) architectures. Work with data pipelines, vector databases, and search algorithms. Manage document chunking and embedding selection for optimal context window usage.”