Job Description
Are you ready to shape the future of artificial intelligence? Nebula AI Systems is seeking a visionary Senior Generative AI Engineer to lead our next-generation language model initiatives. We are not just building tools for today; we are architecting the intelligent infrastructure required for 2026 and beyond.
In this high-impact role, you will be at the forefront of the Generative AI revolution, leveraging cutting-edge Large Language Models (LLMs) to solve complex business problems. You will define the technical roadmap for our AI products, ensuring they are scalable, secure, and capable of redefining user experiences.
Why Join Us?
We offer a competitive compensation package, equity opportunities, and the chance to work with world-class talent in a fully remote-first environment. If you are passionate about pushing the boundaries of what AI can achieve, we want to hear from you.
Responsibilities
- Architect & Develop: Design and implement scalable Large Language Model (LLM) architectures and fine-tuning pipelines using PyTorch and TensorFlow.
- RAG Pipelines: Lead the development of Retrieval-Augmented Generation (RAG) systems to maximize factual accuracy and reduce hallucinations.
- Production Deployment: Oversee the deployment of AI models into production environments using Kubernetes and Docker, ensuring high availability and low latency.
- Data Strategy: Collaborate with data engineering teams to curate high-quality training datasets and implement data governance frameworks.
- Ethical AI: Establish rigorous testing protocols for bias detection, fairness, and safety compliance in AI outputs.
- System Optimization: Continuously optimize model inference performance and resource utilization for cost-effective scaling.
- Mentorship: Mentor junior data scientists and engineers, fostering a culture of technical excellence and innovation.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning Engineering or AI Research.
- Technical Skills: Deep proficiency in Python, PyTorch, and Hugging Face Transformers.
- Frameworks: Extensive experience with MLOps tools (MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- NLP Expertise: Strong understanding of Natural Language Processing (NLP) concepts, tokenization, and transformer architectures.
- Problem Solving: Demonstrated ability to troubleshoot complex technical challenges and deliver production-ready solutions.
- Communication: Excellent verbal and written communication skills for translating technical concepts to non-technical stakeholders.