Job Description
We are seeking a visionary Senior Generative AI Engineer to lead our next-generation AI initiatives. In a world rapidly approaching the age of autonomous intelligence, your work will define the standard for ethical and scalable AI systems. You will be part of an elite team pushing the boundaries of what is possible with Large Language Models (LLMs) and multimodal AI.
At Apex Future Technologies, we don't just predict the future; we engineer it. If you have a passion for building AI that understands context, nuance, and human emotion, we want to meet you.
Responsibilities
- Model Architecture: Design, train, and fine-tune state-of-the-art transformer models and generative architectures to handle complex, multi-modal data inputs.
- System Optimization: Engineer high-performance inference pipelines capable of handling millions of requests with sub-millisecond latency and minimal latency.
- Ethical AI: Implement robust guardrails and safety mechanisms to ensure AI outputs align with human values and regulatory standards.
- Research & Development: Stay at the forefront of AI research, experimenting with novel techniques like reinforcement learning from human feedback (RLHF) and self-supervised learning.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of innovation, technical excellence, and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field from a top-tier institution.
- Experience: 5+ years of experience in software engineering, with at least 3 years dedicated to AI/ML research or production implementation of LLMs.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers. Experience with LangChain and vector databases (e.g., Pinecone, Weaviate).
- Domain Knowledge: Deep understanding of NLP, RAG (Retrieval-Augmented Generation), prompt engineering, and model deployment strategies.
- Problem Solving: Proven track record of solving complex algorithmic problems and scaling systems from prototype to production in a cloud environment.