Machine Learning Engineer

As an AI Engineer, you will be responsible for designing, building, and deploying cutting-edge Large Language Model systems. You will go beyond API integrations to tackle the hands-on challenges of adapting, fine-tuning, and optimizing open-source models for your specific domain. Your work will be the cornerstone of AI-driven features, directly impacting users and product capabilities. - Design & Implementation: Lead the end-to-end development of the LLM stack, including prototyping to production. Fine-tune state-of-the-art open-source models using techniques like LoRA, QLoRA, and full-parameter fine-tuning. - RAG System Development: Build and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy with proprietary data. - Model Optimization: Optimize models for inference, focusing on reducing latency and cost through quantization, pruning, and compilation. - Data Curation & Engineering: Build robust data pipelines for collecting, cleaning, and structuring training data for instruction-tuning and alignment. - Evaluation & Experimentation: Develop benchmarking frameworks to evaluate model performance, mitigate hallucination, and ensure robust and reliable systems. - Production Deployment: Collaborate with MLOps and backend teams to deploy, monitor, and maintain models in a live cloud environment. - Technical Leadership: Stay ahead by researching and implementing the latest advancements from the open-source community and academic papers. You are a pragmatic builder with a deep passion for AI. You have a strong foundation in machine learning, proficiency in Python and key ML frameworks, and enjoy making models work efficiently in a real-world setting. - Bachelor's or Master's in Computer Science, AI, or related field, or equivalent proven experience. - 2+ years of hands-on experience in building and deploying machine learning models. - Strong proficiency in Python and deep learning frameworks like PyTorch or TensorFlow. - Proven experience in fine-tuning and deploying Large Language Models. - Solid understanding of the Transformer architecture and modern NLP. - Experience with the Hugging Face ecosystem and vector databases. - Familiarity with working in a cloud environment and containerization. Bonus Points (Nice-to-Have): - Experience with LLM inference optimization tools. - Experience with LLM evaluation frameworks and benchmarks. - Knowledge of reinforcement learning or direct preference optimization. - Contributions to open-source AI projects or a strong portfolio of personal projects. - Publications in relevant ML venues. The company offers a competitive salary and equity package, the opportunity to work on foundational AI technology with a high degree of ownership, access to state-of-the-art hardware, and a collaborative, fast-paced environment that values excellence.,