Senior Artificial Intelligence Engineer – Voice & LLMs

LLM Development and Fine-Tuning

• Fine-tuning large language models (OpenAI, Anthropic, Meta) for natural and contextual voice interactions.

• Designing NLP pipelines tailored to real-time dialogue and production constraints.

• Customizing models for specific use cases, optimizing for accuracy, consistency, and performance.

• Conducting continuous technology watch to stay up to date with the latest AI advancements.

Advanced Prompting & Real-Time AI Content Generation

• Creating and optimizing prompts for precise and contextually appropriate responses.

• Implementing prompt chaining strategies and few-shot learning when needed.

Python Development, APIs & Deployment

• Building and maintaining scripts, notebooks, and APIs to integrate models into our voice applications.

• Using modern Python tools such as Pydantic for data validation and structuring, and Instructor for fine-tuning and embedding optimization.

• Optimizing algorithms and models to enhance accuracy and efficiency.

• Deploying models and pipelines in production, ensuring performance, scalability, and reliability.

• Collaborating with development teams to integrate AI models into existing products and services.

• Working with backend and DevOps teams to ensure continuous monitoring, maintenance, and optimization of models.

Performance and Latency Optimization

• Analyzing and improving the response times of AI models and pipelines for voice interactions.

• Profiling and tuning models for smooth real-time operation on LiveKit, Twilio, and other voice platforms.

Integration of External Features and Data Sources

• Designing pipelines that allow voice agents to leverage various features or data sources: vector databases, web search, SMS sending, third-party API integrations, etc.

• Developing modular and scalable solutions to enrich model responses based on context and business needs.

• Managing consistency, latency, and security when accessing external data or performing AI-triggered actions.

Real-Time Machine Learning & NLP

• Training, deploying, and optimizing ML and NLP models for low-latency systems.

• Monitoring model performance and continuously improving production pipelines.

• Degree in Computer Science, Applied Mathematics, AI, or a related field.

• Minimum of 5 years of experience in AI, ML, or NLP, with a strong track record in production and model deployment.

• Expertise in Python and ML/NLP libraries (PyTorch, TensorFlow, HuggingFace, LangChain, etc.), as well as modern tools (Pydantic, Instructor).

• Proven experience in LLM fine-tuning and Prompt Engineering.

• Knowledge of pipelines integrating external functionalities (RAG, APIs, third-party tools, web scraping, SMS, etc.).

• Experience with real-time or interactive systems (latency-critical, performance optimization).

• Ability to design, deploy, and maintain models in production.

• Autonomy, rigor, and a passion for tackling complex technical challenges.

Additional Assets

• Experience with voice platforms (LiveKit, Twilio, WebRTC).

• Experience with MLOps and CI/CD pipelines for ML workflows.

• Familiarity with deployment frameworks (FastAPI, BentoML, TorchServe, etc.).

• Knowledge of cloud environments (GCP).