AI Engineer Lead at Orica managing and optimizing AI models and datasets while mentoring engineers. Bridging R&D, Engineering, and Data Platforms in a collaborative environment.
About the role
- Lead design and development of advanced AI systems in Giza. Focus on LLM fine-tuning, RAG, and multidisciplinary AI solutions.
Responsibilities
- Design, develop, and deploy AI agents capable of performing autonomous tasks.
- Build and fine-tune solutions using LLMs (e.g., OpenAI, Gemini, Claude, Mistral, LLaMA).
- Proficient in Retrieval-Augmented Generation (RAG) systems and experienced with vector databases, leveraging these technologies to enhance AI model performance and scalability.
- Implement communication between agents using agent orchestration frameworks.
- Automate workflows for sales, project management, accounting, and localization.
- Collaborate with backend and frontend teams to integrate AI services.
- Conduct prompt engineering and evaluation of model outputs.
- Ensure the scalability and performance of AI systems in production environments.
Requirements
- 7+ years of experience in AI, NLP, or ML system development.
- Proven expertise in LLM training and fine-tuning (GPT, LLaMA, Mistral, Falcon, etc.).
- Hands-on experience with RAG pipelines and vector databases (FAISS, Pinecone, Qdrant, Weaviate).
- Strong Python skills and proficiency in PyTorch or TensorFlow.
- Experience building and deploying custom AI models for text or speech.
- Knowledge of transformer architectures, tokenization, and quantization.
- Practical use of OpenAI API, Hugging Face, and LangChain.
- Experience integrating models via FastAPI, Flask, or NestJS microservices.
- Familiarity with MLOps, Docker, CI/CD, and cloud environments (AWS/GCP/Azure).
- Strong foundation in deep learning, statistics, and data preprocessing.
- Excellent communication and leadership skills to guide junior AI engineers.
- Nice to Have: Experience with speech models (Deepgram, Whisper, Piper).
- Familiarity with Twilio, Asterisk, or WebSocket-based AI streaming.
- Knowledge of localization automation and translation quality scoring.
- Understanding of LangGraph, CrewAI, or multi-agent frameworks.
- Exposure to multi-GPU training and on-premise optimization.
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