Job Description
Are you ready to architect the intelligence of tomorrow? At Nebula Future Systems, we are building the foundational AI infrastructure for the year 2026 and beyond. We are seeking a visionary Senior AI/ML Engineer to lead our Generative AI and Agentic Workflows division.
In 2026, AI evolves beyond simple chatbots into autonomous, reasoning agents. We are looking for a technical leader who thrives in ambiguity and is obsessed with pushing the boundaries of Large Language Models (LLMs), reinforcement learning, and ethical AI deployment. You will not just write code; you will define the standard for next-generation artificial intelligence.
Why join us?
We offer top-tier compensation, stock options, and the opportunity to work on projects that will reshape the global digital landscape.
Responsibilities
- Architect Next-Gen Models: Design and implement scalable Generative AI architectures using LLMs, Transformers, and diffusion models.
- Optimize Performance: Fine-tune and distill large models to ensure high accuracy, low latency, and cost-efficient inference in production environments.
- Build Autonomous Agents: Develop Agentic workflows that enable AI systems to perform complex, multi-step reasoning and tool use independently.
- Improve RAG Systems: Enhance Retrieval-Augmented Generation pipelines to reduce hallucinations and improve factual grounding.
- Model Governance: Establish best practices for AI safety, fairness, and transparency in model training and deployment.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in NLP, Deep Learning, or Generative AI.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with Hugging Face Transformers.
- MLOps: Strong understanding of MLOps, data pipelines, and model deployment strategies (Kubernetes, Docker, SageMaker, etc.).
- Tools: Deep knowledge of Vector Databases (Pinecone, Milvus, Weaviate) and RAG frameworks.