Job Description
Join Apex Innovations, a forward-thinking leader in artificial intelligence, as we architect the next generation of intelligent systems. We are looking for a visionary Senior AI Engineer to lead the development of cutting-edge Generative AI models. If you are passionate about the intersection of data science and creative engineering, this is your chance to shape the future of technology in 2026 and beyond.
Why Join Us?
- Work on groundbreaking projects that define the industry standard.
- Competitive compensation package and equity options.
- Flexible remote-first culture with premium office amenities.
The Role
You will be responsible for the end-to-end lifecycle of our proprietary LLMs, from research and training to deployment and optimization. You will collaborate with cross-functional teams of researchers, product managers, and engineers to deliver scalable AI solutions.
Responsibilities
- Model Development: Design, train, and fine-tune large language models (LLMs) using PyTorch and TensorFlow to achieve state-of-the-art performance.
- RAG Architecture: Implement and optimize Retrieval-Augmented Generation (RAG) systems to enhance model accuracy and reduce hallucinations.
- Deployment: Deploy models to production environments using containerization (Docker/Kubernetes) and serverless architectures.
- Performance Optimization: Conduct rigorous testing and profiling to ensure models run efficiently on edge devices and cloud infrastructure.
- Research: Stay at the forefront of AI research, experiment with novel architectures, and publish findings to the open-source community.
- Collaboration: Partner with data scientists and software engineers to integrate AI capabilities into client-facing products.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning engineering, specifically in NLP and Generative AI.
- Technical Skills: Deep proficiency in Python, C++, and modern deep learning frameworks (PyTorch, JAX, Hugging Face).
- Architecture: Strong understanding of transformer architectures, attention mechanisms, and model quantization techniques.
- Tools: Experience with MLOps pipelines, cloud platforms (AWS/GCP), and vector databases (Pinecone, Milvus).
- Soft Skills: Excellent problem-solving abilities and the ability to communicate complex technical concepts to non-technical stakeholders.