Artificial Intelligence/Machine Learning Engineer - LLM/Python (5-6 yrs)

About the Role / What You Will Own :

You will be creating the most human-like AI Friend ever! You'll be at the heart of our AI friend, making sure it feels alive, responsive, and deeply human-like in both text and voice.

This means fine-tuning conversational AI models, optimizing real-time voice interactions, and ensuring the AI is lightning fast.

You'll collaborate closely with the Full-Stack Engineer to bring seamless, real-time AI-powered interactions to both web and mobile users.

What You'll Be Doing :

- Fine-tune LLMs (Mistral, LLaMA 3, OpenAI API) to make AI conversations feel natural, engaging, and human-like.

- Build and optimize real-time AI voice interactions-users should be able to chat with AI like they're on a call.

- Integrate speech models like Whisper API for speech-to-text and Azure TTS for text-to-speech.

- Use Vector Database (pgvector) to ensure efficient storage of AI embeddings and user interactions for quick retrieval and personalization.

- Develop and maintain AI-powered APIs with FastAPI for web and mobile apps.

- Ensure fast response times through caching, query optimization, and scalable architecture.

- Deploy AI models on Azure to handle thousands of concurrent users smoothly.

- Optimize AI performance for fast, context-aware, and scalable responses.

- Work on AI-driven onboarding so users feel an instant connection with their AI friend from the very first interaction.

Tech Stack :

- Tech Stack.

- Programming Language - Python.

- Backend Framework - FastAPI (lightweight, async, high-performance).

- Machine Learning Framework - PyTorch (for LLM fine-tuning & speech processing).

- Vector Database - pgvector (PostgreSQL extension for vector search).

- Short-Term Memory Cache - Redis (for quick data retrieval).

- LLM (Large Language Model) - Fine-tuned OpenAI API / LLaMA 3 / Mistral.

- Speech-to-Text (STT) & Text-to-Speech (TTS) - Whisper API + Azure TTS.

- Database - PostgreSQL (hosted on Supabase).

- Cloud Platform - Azure (for hosting APIs, storage, and compute).

- Orchestration - Docker + Kubernetes (for scalability).

- Logging & Monitoring - Prometheus + Grafana Must-Have Skills AI & Machine Learning.

Must have skills :

- AI & Machine Learning.

- Experience fine-tuning LLMs (Mistral, LLaMA 3, OpenAI API) for real-time applications.

- Hands-on expertise with PyTorch and AI model optimization.

- Speech Processing & Voice AI.

- Experience integrating speech-to-text and text-to-speech APIs (Whisper, Azure TTS, Google TTS).

- Knowledge of real-time voice processing and.

- AI-driven speech interactions.

- Backend AI Development & Vector Database.

- Strong Python + FastAPI experience for building and scaling AI-powered APIs.

- Proficiency with Supabase (PostgreSQL + pgvector) for AI conversation storage.

- Performance & Optimization.

- Experience with Redis for caching and API performance tracking (Prometheus + Grafana).

- Scalable AI deployment on Azure.

Good-to-Have Skills :

- AI Personalization: Experience implementing adaptive AI that learns user preferences over time.

- AI UX Experience: Knowledge of structuring AI-generated responses to feel more natural.

What Success Looks :

- Like AI That Feels Human: The AI responds naturally, remembers past interactions, and adapts to users.

- Seamless Voice Conversations: Users can talk to AI in real time, with instant and natural-sounding responses.

- A Fast, Scalable AI System: AI replies are instant, smooth, and can handle thousands of users at once.