Job Description
Join the Architects of Tomorrow.
At Apex Future Systems, we are not just building software for today; we are engineering the core intelligence layer for the year 2026 and beyond. We are looking for a visionary Senior Generative AI Engineer to lead the development of next-generation Large Language Models (LLMs) and autonomous agents.
In this role, you will define the roadmap for our proprietary AI infrastructure, pushing the boundaries of what is possible in natural language processing, multimodal synthesis, and ethical AI alignment.
Why Join Us?
- Work on cutting-edge technology that will define the future.
- Competitive equity package and performance bonuses.
- Flexible remote-first policy with a hub in San Francisco.
Responsibilities
- Architect & Deploy: Design and implement scalable generative AI pipelines using modern deep learning frameworks (PyTorch, TensorFlow, JAX).
- Model Optimization: Fine-tune and optimize large foundation models for specific enterprise use cases, improving inference speed and reducing token costs.
- R&D Leadership: Lead internal research initiatives exploring novel architectures such as Mixture of Experts (MoE) and retrieval-augmented generation (RAG) systems.
- Performance Engineering: Collaborate with the MLOps team to ensure production-grade reliability, monitoring, and A/B testing of AI models.
- Ethical AI: Implement guardrails and safety protocols to ensure AI outputs are unbiased, safe, and compliant with emerging regulations.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of professional experience in deep learning, NLP, or machine learning engineering.
- Technical Skills: Proficiency in Python, C++, and experience with Hugging Face Transformers, LangChain, or Vector Databases (Pinecone, Milvus).
- Frameworks: Strong hands-on experience with training, fine-tuning, and deploying LLMs (e.g., GPT-4, Llama 3, Claude).
- Problem Solving: Demonstrated ability to tackle complex mathematical and algorithmic challenges in high-pressure environments.