Job Description
Are you ready to architect the future of intelligence?
Nexus Future Labs is at the forefront of the AI revolution. As we gear up for our 2026 roadmap, we are seeking a visionary Senior AI Engineer to lead our next-generation LLM and generative AI initiatives. You will not just be building models; you will be defining the ethical frameworks and scalable architectures that will power the world's most advanced AI systems.
In this role, you will work directly with our CTO and product leaders to integrate cutting-edge deep learning techniques into our production ecosystem. If you are passionate about pushing the boundaries of what is possible with artificial intelligence, this is your chance to make an indelible mark on the industry.
Responsibilities
- Architect and deploy scalable deep learning models and Large Language Models (LLMs) for high-traffic production environments.
- Design and implement robust Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Optimize model inference latency and resource consumption using techniques such as quantization, pruning, and distillation.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate complex AI concepts into user-centric solutions.
- Stay ahead of the curve by researching emerging AI methodologies, including multimodal learning and autonomous agents.
- Mentor junior engineers and establish best practices for MLOps and model governance.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in AI/ML engineering, with a focus on deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Proven track record of shipping production-grade AI applications and models.
- Expert knowledge of Python, SQL, and distributed computing systems (Kubernetes, Docker).
- Familiarity with modern LLM frameworks (LangChain, LlamaIndex) and vector databases (Pinecone, Milvus, Weaviate).
- Strong understanding of NLP, transformer architectures, and neural network optimization.