Job Description

Job Title: Machine Learning Engineer I

Location: Remote

Duration: 6 Months

Enhancing and building out LLM and AI model to replicate and eventually replace what is existing it was built more in a linear fashion.

Must Haves:

• Experience working with LLMs
• Working with APIs
• Python Experience
• 4-5 years of experience

Client provided description:

Junior prompt engineer who will design, test, and refine prompts for AI models to improve their outputs, requiring strong communication, analytical, and creative skills, combined with a foundational understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and basic programming (often Python).

Skills Required:

• AI Concepts - A strong foundational understanding of LLMs, machine learning, deep learning, and NLP principles
• Basic Programming - Proficiency in Python for interacting with AI APIs, automation, and testing is often required.
• AI Tools: Familiarity with common AI platforms and models like GPT, Google Gemini, and tools such as OpenAI Playground.
• Data Analysis: Ability to analyze AI outputs, identify biases, and understand the data used in prompts and generated by the AI.
• Other: Collaborating with various teams, experimenting with different prompt structures, analyzing results, and staying updated on AI advancements under the guidance of product leads and senior engineers.

Business Benefit of Investment:

This resource will join the TTX ITG and Conversational Paths product teams, who will be partnering with various teams to develop, tune, and maintain both customer and agent-facing conversational paths within the Google AI platform of tools. This resource is critical in providing initial engineering-level understanding and guidance to be supporting by existing ITG and Paths team resources. The ultimate goal is to enhance the user experience (customer and agent) in integrating with the Celestial and Mercury architecture to continue to build toward world-class troubleshooting and problem solving experience for our users.

Key Deliverables:

• Support and influence the technical build to replace deterministic flows with AI-based conversational flows.
• Provide guidance and suggestions for ETE architectural considerations for optimizing of reusability and optimization of data sourcing
• Assist ITG and Paths teams in building net new use cases, as well as re-designing and converting existing flows from deterministic to conversational/intelligent

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