Job Description
Are you ready to engineer the future? Horizon 2026 Technologies is looking for a visionary Senior Generative AI Engineer to spearhead our next wave of intelligent systems. As we look toward the technological horizon of 2026, we need a technical leader to build scalable, responsible, and high-performance AI models that define the next generation of user experiences.
We are not just building software; we are constructing the cognitive layer of the digital world. If you are passionate about Large Language Models (LLMs), transformers, and the ethical implications of artificial general intelligence, we want to meet you.
Why Join Us?
- Work on projects that will shape the industry standard for 2026 and beyond.
- Competitive compensation package including equity and remote-first flexibility.
- Access to cutting-edge hardware and proprietary datasets.
Responsibilities
- Lead the end-to-end development and deployment of Generative AI models, including fine-tuning and RAG (Retrieval-Augmented Generation) architectures.
- Design scalable MLOps pipelines to ensure model reliability, monitoring, and continuous learning.
- Collaborate with cross-functional teams of data scientists, product managers, and designers to translate business requirements into technical AI solutions.
- Research and implement novel techniques in Natural Language Processing (NLP) to improve model accuracy and reduce hallucinations.
- Optimize inference latency and cost-efficiency for production-grade environments.
- Establish best practices for AI ethics, bias mitigation, and data privacy in all development cycles.
Qualifications
- Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- 5+ years of professional experience in software engineering, with at least 3 years specifically focused on AI/ML.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of transformer architectures and Large Language Models (GPT, BERT, Llama, etc.).
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of shipping production-level machine learning models.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.