Job Description
We are on a mission to redefine the technological landscape for the year 2026. Nebula AI is seeking a visionary Senior AI/ML Engineer to lead the development of next-generation generative AI systems. In this pivotal role, you will architect scalable machine learning infrastructure, push the boundaries of LLM capabilities, and ensure our solutions are robust, ethical, and ready for the future.
Join a team of world-class researchers and engineers working on the forefront of artificial intelligence. You will have the opportunity to influence the strategic direction of our products and directly impact how users interact with AI in the coming years.
Responsibilities
- Architect Scalable Models: Design and implement state-of-the-art deep learning architectures, with a focus on Large Language Models (LLMs) and multimodal AI systems.
- Optimize Performance: Engineer high-performance inference pipelines to reduce latency and improve throughput for real-time AI applications.
- Collaborate & Innovate: Partner with product managers and engineers to translate complex business requirements into cutting-edge AI technical solutions.
- Ethical AI Governance: Establish and enforce best practices for data privacy, model governance, and bias mitigation to ensure responsible AI deployment.
- Technical Leadership: Mentor junior engineers, conduct code reviews, and contribute to the technical roadmap for 2026 and beyond.
- Research Integration: Stay ahead of industry trends in generative AI and integrate novel research findings into production environments.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in machine learning engineering, data science, or a similar technical role.
- Technical Stack: Expert proficiency in Python, PyTorch, or TensorFlow.
- Infrastructure: Strong experience with MLOps, cloud platforms (AWS/GCP/Azure), and containerization technologies (Docker/Kubernetes).
- Model Engineering: Proven track record of deploying production-grade ML models and optimizing training workflows.
- AI Specialization: Deep understanding of LLM fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.