Job Description
Shape the Future of Intelligence
We are looking for a visionary Senior AI/LLM Engineer to join our elite team at Nexus Future Systems. As we project toward the technological landscape of 2026, we are building the next generation of autonomous, multimodal AI agents. If you are passionate about pushing the boundaries of Large Language Models (LLMs), optimizing inference at scale, and defining the standard for AGI (Artificial General Intelligence), we want to meet you.
In this role, you will bridge the gap between cutting-edge research and production-grade software engineering. You will architect systems that not only understand context but also act intelligently in dynamic environments.
Responsibilities
- Architect Scalable LLM Infrastructures: Design and implement robust, distributed systems for training and fine-tuning large language models using PyTorch and TensorFlow.
- Pipeline Optimization: Engineer high-performance inference pipelines to reduce latency and cost, utilizing techniques like model quantization, pruning, and distillation.
- Agentic Workflows: Develop and integrate autonomous agent frameworks capable of complex reasoning, tool use, and multi-step planning.
- RAG & Vector Search: Lead the development of advanced Retrieval-Augmented Generation systems to enhance model accuracy and reduce hallucinations.
- Safety & Alignment: Implement rigorous safety guardrails and RLHF (Reinforcement Learning from Human Feedback) processes to ensure AI outputs are ethical and aligned with human values.
- Collaboration: Partner with product managers, data scientists, and researchers to translate research concepts into deployable software solutions.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related technical field.
- Experience: 5+ years of professional experience in software engineering, with at least 3 years specifically focused on AI/ML or NLP.
- Technical Stack: Proficiency in Python, PyTorch, and modern deep learning frameworks. Experience with cloud platforms (AWS/GCP/Azure) and Kubernetes is required.
- LLM Expertise: Deep understanding of Transformer architectures, attention mechanisms, and the current state-of-the-art in generative AI models.
- Problem Solving: Proven ability to debug complex distributed systems and optimize performance bottlenecks under pressure.
- Communication: Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.