AI Developer / AI Engineer

What You'll Do As a founding AI engineer at Peppr AI, you'll take ownership of designing, developing, and deploying intelligent systems that form the core of our agentic AI platform. You'll build ML/LLM pipelines, integrate AI agents into production systems, and collaborate closely across UX, infrastructure, and backend domains to create seamless AI-powered enterprise experiences.

Key Responsibilities Include

• Develop end-to-end AI pipelines: data ingestion, context engineering, retrieval-augmented generation (RAG), model integration, validation, and monitoring
• Design and optimize low-latency voice agents, including STT, VAD, TTS, and streaming solutions using WebRTC or WebSockets
• Implement production-grade AI features, integrating them with FastAPI endpoints, Weaviate vector databases, and secure authentication systems
• Collaborate with frontend and backend teams to deliver AI-powered user experiences
• Continuously evaluate and prototype emerging frameworks (e.g., Hugging Face, LangChain, LLaMA) to improve performance and scalability
  
  

Required

What We're Looking For We're seeking a builder excited to push the boundaries of AI in enterprise settings. You should have:

• 3 years of experience in AI/ML engineering, with hands-on experience in building and deploying LLM-based systems
• Strong proficiency in Python and familiarity with frameworks such as Hugging Face Transformers and LangChain
• Practical experience with Weaviate or similar vector databases for RAG-based systems
• Understanding of low-latency voice technologies, including streaming pipelines and real-time audio processing
• Solid background in infrastructure: Docker, Kubernetes, CI/CD, monitoring, and secure API development
  
  

Preferred

• Experience building multi-step agent orchestration or custom AI workflows
• Knowledge of latency reduction techniques (e.g., model quantization, caching)
• Familiarity with designing voice-first conversational UX
• Strong understanding of security, data protection, and adversarial considerations in AI pipelines
• A product mindset and experience shipping end-to-end features
• Comfort with ambiguity and rapid iteration in a startup environment