Process Engineer

Chubb Insurance Hong Kong; España; On-site; PHP 2,460,024 - 3,690,037; Full time

Job summary

Chubb Insurance Hong Kong is seeking a qualified Prompt Engineer and LLM Integration specialist in the Philippines. This position involves optimizing interactions with Large Language Models (LLMs), primarily through Python development.

The ideal candidate will possess a strong understanding of OpenAI's APIs and be adept at managing tokens. This role requires experimentation with various LLMs to achieve optimal performance, making it a unique opportunity for those proficient in prompt engineering and results analysis.

Qualifications

• Experience in optimizing interactions with LLMs through Python.
• Deep understanding of OpenAI's APIs.
• Ability to analyze and improve model configurations.

Responsibilities

• Optimize token usage in LLM interactions.
• Develop and maintain Python code for API interactions.
• Evaluate and compare LLMs and prompts for best solutions.

Skills

Token Management

API Integration

LLM Experimentation

Prompt Engineering

Python

Job description

We are looking for a qualified Prompt Engineer and LLM Integration specialist with experience in optimizing interactions with Large Language Models (LLMs), including ChatGPT, through Python. The ideal candidate will have a deep understanding of OpenAI's APIs, experience in token management, and the ability to work with multiple LLMs and prompts to achieve the best results. A high level of experimentation and analysis is required for this position. (12-month job Opportunity)

Profile

• Token Management: Optimize token usage in interactions with LLMs, taking into account context limitations and associated costs.
• API Integration: Develop and maintain Python code that efficiently interacts with OpenAI's APIs to access different LLMs.
• LLM Experimentation: Evaluate and compare the performance of different LLMs and prompt configurations to identify the best solutions for each use case.
• Multiple Prompt Management: Design strategies for managing and executing multiple prompts in parallel or sequentially, including combining results from different LLMs.
• Prompt Engineering: Design, refine, and optimize prompts for different LLMs (including GPT-3.5-turbo, GPT-4, and potentially other models) with the goal of obtaining accurate, relevant, and coherent responses for a variety of tasks.
• Results Analysis: Analyze the responses from LLMs, identifying patterns and areas for improvement in prompts and model configurations.
• Documentation: Document experiments, results, and best practices related to prompt engineering and LLM integration.