Job Description
We are on a mission to define the artificial intelligence landscape of 2026. Nexus Future Labs is seeking a visionary Senior Generative AI Engineer to lead the development of next-generation Large Language Models (LLMs) and Agentic AI systems. You will not just be building tools; you will be architecting the cognitive architecture that powers our enterprise solutions.
If you are passionate about pushing the boundaries of what is possible with PyTorch, Transformers, and Retrieval-Augmented Generation (RAG), we want to hear from you. Join a team of elite engineers dedicated to solving the hardest problems in AI alignment, scalability, and multimodal understanding.
Responsibilities
- Architect Scalable AI Infrastructure: Design and implement robust, high-throughput inference pipelines for LLMs, focusing on optimization and latency reduction.
- Model Development & Fine-tuning: Spearhead the fine-tuning and alignment of open-source models (e.g., Llama 3, Mistral) and proprietary architectures using advanced techniques like LoRA and QLoRA.
- Agentic Workflow Design: Develop autonomous AI agents capable of complex multi-step reasoning and tool utilization.
- RAG Pipeline Optimization: Engineer state-of-the-art Retrieval-Augmented Generation systems to ensure factual accuracy and reduced hallucinations.
- MLOps & Deployment: Establish CI/CD pipelines for machine learning, ensuring seamless deployment to production environments.
- Research & Innovation: Stay ahead of the curve on emerging AI trends, including Multimodal AI and Reinforcement Learning from Human Feedback (RLHF).
Qualifications
- Experience: 5+ years of professional software engineering experience with at least 2 years specifically focused on Machine Learning and Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, and TensorFlow. Deep understanding of Hugging Face Transformers and ecosystem.
- Database Mastery: Extensive experience with Vector Databases (Pinecone, Milvus, Weaviate) and NoSQL solutions.
- Education: MS or PhD in Computer Science, Artificial Intelligence, or a related quantitative field (or equivalent practical experience).
- Problem Solving: Strong ability to debug complex distributed systems and optimize performance bottlenecks.
- Communication: Excellent written and verbal communication skills, capable of translating technical concepts for non-technical stakeholders.