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Machine Learning Engineer for Conversational AI App
Looking for a Machine Learning Engineer (MLE) to help us optimize and scale the AI backend of our conversational AI application. This app enables users to engage in real-time conversations with AI-driven voice agents. Require detailed reports on testing outcomes, including performance benchmarks, failure cases, and recommendations for improvement based on hands-on evaluation. We're already leveraging tools like Eleven Labs, Firebase, and LLMs, but now we need an MLE to fine-tune models, compare AI platforms, and bring documented structure to our growing AI stack. = = = Responsibilities: • Build and fine-tune LLMs or integrate third-party APIs to simulate realistic conversations. • Ensure the AI supports rebuttal trees, conversation memory, and realistic voice tone. • Test AI configurations within the app, identify weak points or failures, and propose clear solutions. • Optimize latency and accuracy in AI voice conversations to enhance response capabilities. • Document AI tools based on quality, speed, performance, pricing, and scalability. • Document prompting systems and model configurations. • Create clear documentation so non-engineers can understand and use AI workflows. • Keep the engineering and product teams informed about AI technology and different model capabilities. It is required that you document your workflow for both developers and non-developers alike. Create a written guide on how to update prompts, models, and AI configs. We are looking for someone with experience building and scaling voice agents, call simulation models, and sales training platforms. = = = AI Platforms & Tools of Interest: We’re actively exploring AI platforms: • ElevenLabs.io • Bland.ai • OpenAI (GPT-4) • Anthropic Claude • Hugging Face • Whisper AI • LangChain • LlamaIndex • LM Studio • RabbitMQ = = = REQUIRED DAILY MEETING: You are required to attend a daily stand-up meeting at 9:30 AM EST or 11:30 AM EST, Monday through Friday.