jobTitle

skills

GenAI Engineer

<div><strong style="color: rgb(239, 72, 21);">Role and responsibilities</strong></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, and system integrations</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span><strong style="color: black;">Utilize, apply &amp; enhance GenAI models</strong><span style="color: black;"> using state-of-the-art techniques like transformers, GANs, VAEs, and LLM models</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Implement and optimize GenAI models for performance, scalability, and efficiency</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span><strong style="color: black;">Integrate GenAI models, including LLMs, into production pipelines, applications, and existing analytical solutions</strong></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Develop </span><strong style="color: black;">user-facing interfaces and APIs to interact with GenAI models, including LLMs</strong></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Utilize </span><strong style="color: black;">prompt engineering techniques</strong><span style="color: black;"> to enhance model performance, including LLM models</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Apply software engineering principles to develop robust, scalable, and maintainable GenAI applications</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Build and deploy GenAI applications on cloud platforms</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Integrate GenAI applications with other applications, tools, and analytical solutions to create a cohesive user experience and workflow</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Continuously evaluate and improve GenAI models and applications based on data, feedback, and user needs</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Stay up-to-date with the latest advancements in GenAI research, development, software engineering practices, and integration tools</span></div><div><span style="color: rgb(26, 26, 26);">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span><span style="color: black;">Document code, models, and processes for future reference</span></div><div><span style="color: rgb(26, 26, 26);">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.</span></div><div><span style="color: rgb(26, 26, 26);">&nbsp;</span></div><div><strong style="color: rgb(239, 72, 21);">Technical skills requirements</strong></div><div><span style="color: black;">The candidate must demonstrate proficiency in,</span></div><div><span style="color: black;">&nbsp;</span></div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<strong>Use, apply, enhance GenAI models</strong> using state-of-the- art techniques like transformers, GANs, VAEs, LLMs (including <strong>experience with various LLM architectures and capabilities</strong>), and <strong>vector representations</strong> for efficient data processing.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Implement and optimize GenAI models for performance, scalability, and efficiency, considering factors like <strong>chunking strategies for large datasets</strong> and efficient memory management.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Integrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and <strong>RAG workflows</strong>, ensuring seamless data flow and information exchange.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<strong>Develop user-facing interfaces and APIs</strong> (RESTful or GraphQL) to interact with GenAI models and RAG applications, providing a user-friendly experience.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Utilize <strong>LangChain and similar tools</strong> (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Apply software engineering principles to develop robust, scalable, maintainable, and production-ready GenAI applications.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<strong>Build and deploy GenAI applications on cloud platforms</strong> (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Integrate GenAI applications with other applications, tools, and analytical solutions (including dashboards and reporting tools) to create a cohesive user experience and workflow within the RAG ecosystem.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<strong>Continuously evaluate and improve GenAI models</strong> and applications based on data, feedback, user needs, and RAG application performance metrics.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Stay up-to-date with the latest advancements in GenAI research, development, software engineering practices, integration tools, LLM architectures, and RAG functionalities.</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Document code, models, processes, and RAG application design for future reference and knowledge sharing.</div><div><strong style="color: rgb(239, 72, 21);">&nbsp;</strong></div><div><strong style="color: rgb(239, 72, 21);">Nice-to-have skills</strong></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience working with RAG applications</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience with cloud-based data warehousing solutions (e.g., BigQuery, Redshift, Snowflake)</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Experience with cloud-based workflow orchestration tools (e.g., Airflow, Prefect) </span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Familiarity with Kubernetes (K8S) is a welcome addition</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Google Cloud certification</span></div><div><span style="color: black;">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Unix or Shell scripting</span></div><div><br></div>