Job Description
Shape the Future of Intelligence. Join the 2026 AI Revolution.
Apex Systems is pioneering the next generation of Artificial Intelligence. As a Senior AI/LLM Engineer, you will be at the forefront of developing agentic AI systems and multimodal models designed for the 2026 landscape. This is not just a job; it is an opportunity to define the standards of intelligent automation and build scalable infrastructure for the future.
We are looking for a visionary engineer who thrives in a fast-paced environment and possesses deep expertise in Large Language Models (LLMs) and Generative AI. If you are passionate about pushing the boundaries of what AI can achieve, we want to hear from you.
Why Join Us?
- Next-Gen Technology: Work on cutting-edge AI architectures that are setting the stage for 2026 and beyond.
- Competitive Compensation: Top-tier salary and equity packages for top-tier talent.
- Modern Culture: Collaborative, inclusive, and focused on innovation.
Your Mission:
As a key member of our AI Engineering team, you will be responsible for the full lifecycle of AI model development, from research and prototyping to production deployment and optimization.
Responsibilities
- Design, implement, and optimize scalable LLM architectures for production environments.
- Conduct research and experiments to improve model accuracy, latency, and cost-efficiency for 2026 benchmarks.
- Develop and fine-tune foundation models using proprietary and open-source datasets.
- Collaborate with cross-functional teams (Product, Data Science, Engineering) to integrate AI capabilities into our core products.
- Ensure the ethical use of AI, focusing on bias mitigation, safety, and explainability.
- Mentor junior engineers and contribute to the technical roadmap of our AI division.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field, or equivalent practical experience.
- 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing (NLP).
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Extensive experience deploying LLMs (e.g., GPT, LLaMA, Claude) and implementing RAG (Retrieval-Augmented Generation) pipelines.
- Deep understanding of distributed systems and cloud infrastructure (AWS, GCP, or Azure).
- Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) is highly desirable.