Job Description
The Opportunity
Are you ready to define the landscape of Artificial Intelligence for the year 2026? Apex Future Systems is seeking a visionary Generative AI Engineer to architect and deploy next-generation Large Language Models (LLMs) and multimodal systems. We are building the infrastructure that will power the enterprise of tomorrow, and we need a technical expert to lead our model training and deployment strategies.
Why Join Us?
As we scale towards the 2026 AI standard, we offer a competitive benefits package, equity, and the chance to work on cutting-edge projects that impact millions. You will collaborate with world-class researchers and engineers to push the boundaries of what is possible with generative AI.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art Generative AI models, including LLMs and diffusion models, ensuring high accuracy and safety.
- System Architecture: Build scalable and robust MLOps pipelines for the continuous training and deployment of AI models.
- Prompt Engineering: Develop advanced prompt engineering frameworks to optimize model outputs for specific enterprise use cases.
- Ethical AI: Implement guardrails and safety measures to ensure responsible AI usage and mitigate bias in generated content.
- Performance Optimization: Conduct rigorous testing to reduce latency, improve inference speed, and optimize resource utilization.
- Research Integration: Stay ahead of the curve by integrating the latest research findings from top-tier conferences into our production systems.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Experience: 5+ years of experience in Machine Learning, Deep Learning, or NLP.
- Framework Knowledge: Hands-on experience with Hugging Face Transformers, LangChain, or similar frameworks.
- Cloud Skills: Experience deploying models on cloud platforms (AWS, GCP, or Azure) using containerization (Docker/Kubernetes).
- Problem Solving: Demonstrated ability to tackle complex technical challenges and debug complex ML systems.