Job Title: AI/LLM Engineer (Specialist in Prompt Engineering and Conversational AI)

Location: Remote / Birmingham

Job Type: Full-time

Reports To: Founder

Experience Level: Mid-Senior Level (3+ years in AI/ML development, including LLM experience)

About Us:

We are an innovative, early-stage startup developing a conversational AI-powered e-learning platform designed to revolutionize online education. Our mission is to deliver engaging, highly interactive learning experiences powered by cutting-edge AI and natural language processing (NLP) technologies.

We’re looking for an experienced AI/LLM Engineer who can take the lead in designing and optimizing Large Language Model (LLM) capabilities and managing prompt engineering to drive the conversational AI core of our platform. This role is critical to shaping the voice and behavior of our conversational agent, ensuring it can dynamically adapt to user interactions while offering a personalized, context-aware experience.

If you have experience working with open-source LLMs, conversational AI frameworks, and are passionate about using AI to transform education, we want to hear from you!

Key Responsibilities:

LLM Integration and Development:

Lead the development and integration of Large Language Models (LLMs) (e.g., GPT-3, GPT-J, GPT-Neo, etc.) to enable dynamic, intelligent conversational agents.
Design and execute strategies for prompt engineering to optimize AI outputs and ensure responses are contextually appropriate and aligned with the intended conversational tone (e.g., for user engagement, personality modeling).
Fine-tune and train LLMs on custom datasets (e.g., e-learning content, sales conversations) to ensure the conversational AI is accurate, engaging, and aligned with Rekbot’s tone (rude, abrupt for certain scenarios).
Implement and manage AI-driven personalization features, enabling the bot to adapt to different users based on their learning progress and conversational patterns.

Prompt Engineering and Conversational Flow:

Develop and refine prompts to guide LLMs in delivering the desired rude, abrupt, or dismissive behavior when necessary, while also ensuring seamless conversation flow.
Optimize LLMs to handle user objections, frustrations, and questions, allowing the AI to manage difficult conversations effectively.
Build natural language understanding (NLU) components to improve the AI’s ability to detect user intent, sentiment, and conversation-ending triggers.

Open-Source Tool Integration:

Work with open-source NLP frameworks such as Hugging Face Transformers, Rasa, and Botpress to develop and integrate conversational models.
Leverage open-source vector databases (e.g., Pinecone, Milvus) for dynamic knowledge retrieval and personalized responses based on user input.
Use AI fine-tuning tools and services to customize pretrained models for specific e-learning use cases.

AI Pipeline and Model Optimization:

Develop the architecture and deployment strategies for LLMs in a production environment, ensuring scalability and low-latency interactions.
Ensure performance optimization of conversational AI systems, minimizing response times, and balancing quality with computational efficiency.
Maintain and enhance data pipelines for AI model training and fine-tuning, incorporating user feedback and improving the quality of responses over time.

Collaboration in a Lean Team:

Collaborate with full-stack engineers, frontend developers, and the product team to integrate conversational AI into the overall user experience.
Work closely with the founder/CTO to align on the AI strategy, ensuring that features meet the startup’s goals, particularly under tight budget constraints.
Help define and prioritize AI-related features for both MVP and full-scale development.

Ongoing AI/ML Research and Innovation:

Stay up to date with the latest developments in LLMs, AI/ML models, and NLP tools. Evaluate and integrate the most suitable technologies for the project.
Experiment with cutting-edge AI solutions to improve conversational capability, including experimenting with multi-turn dialogue systems and emotion-aware AI.

Required Skills and Qualifications:

LLM & Prompt Engineering:

3+ years of experience working with Large Language Models (LLMs), including model fine-tuning, deployment, and optimization for conversational AI.
Deep understanding of prompt engineering: ability to craft, test, and optimize prompts to maximize the utility and accuracy of LLMs.
Familiarity with state-of-the-art open-source models like GPT-3, GPT-J, GPT-Neo, or similar.

NLP & Conversational AI:

Hands-on experience developing conversational AI solutions using open-source tools such as Rasa, Dialogflow, Botpress, or similar frameworks.
Experience with building NLU components to detect user intent, manage conversation flow, and handle fallback scenarios.
Knowledge of machine learning techniques for fine-tuning LLMs and improving natural language understanding.

Technical Skills:

Proficiency with Node.Js / Python and ML frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
Experience deploying AI models in cloud environments (e.g., AWS, Google Cloud, or Azure) and optimizing for production workloads.
Strong understanding of RESTful APIs for integrating AI models with frontend and backend systems.

TTS and Speech Capabilities:

Knowledge of integrating Text-to-Speech (TTS) and Speech-to-Text (STT) tools such as Google Cloud TTS, Amazon Polly, or open-source TTS solutions.
Experience developing or integrating voice-based conversational agents in web or mobile applications.

Problem-Solving in a Startup Environment:

Proven ability to work in a lean, fast-paced startup environment, handling multiple responsibilities and making trade-offs based on limited resources.
Comfortable making strategic decisions on AI tools, infrastructure, and trade-offs to meet the demands of an MVP while planning for scalability.

Preferred Qualifications:

Experience with vector databases (e.g., Pinecone, Milvus) for AI-driven knowledge retrieval and conversation management.
DevOps skills: Familiarity with CI/CD pipelines, containerization (Docker), and Kubernetes for AI model deployment.
Experience with real-time conversational analytics: tracking user interactions and optimizing the AI based on feedback.
Experience with human-in-the-loop (HITL) AI workflows to continuously improve conversational AI based on user input.

Who You Are:

AI Enthusiast: You’re passionate about using AI to enhance user experiences, particularly in conversational systems.
Self-Starter: You take ownership of your work and are capable of driving the AI strategy for an early-stage product with minimal supervision.
Collaborative: You work well in small, agile teams and can clearly communicate complex AI concepts to non-technical team members.
Innovative: You thrive on solving tough AI problems and are always looking for ways to leverage cutting-edge technology in creative ways.

Why Join Us?:

Early-Stage Opportunity: Be part of building a product from the ground up, with the chance to shape key aspects of the AI functionality.
High Ownership: You’ll have the autonomy to design and implement core AI features, directly impacting the platform’s success.
Flexible Working Conditions: Work remotely with a highly flexible schedule. We care about output, not hours.

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