Job Description
We are seeking a visionary Senior Generative AI Engineer to lead the development of next-generation artificial intelligence models for our upcoming 2026 roadmap. Apex Future Systems is at the forefront of innovation, building scalable, safe, and efficient Large Language Models (LLMs) that will redefine human-computer interaction. You will have the opportunity to work with a world-class team of researchers and engineers to solve complex problems in natural language processing, computer vision, and multi-modal generation.
Why Join Us?
Join a dynamic, high-growth environment where your work will directly impact the future of technology. We offer competitive compensation, equity packages, and a culture that fosters creativity and continuous learning.
Responsibilities
- Architect and train state-of-the-art generative AI models, including LLMs and diffusion models, tailored for enterprise applications.
- Optimize model inference performance and reduce latency for real-time applications using techniques such as quantization, pruning, and distillation.
- Design and implement robust data pipelines for high-quality training and fine-tuning datasets, ensuring data privacy and compliance.
- Collaborate with cross-functional teams, including product managers, designers, and researchers, to translate technical requirements into scalable solutions.
- Conduct rigorous model evaluation and testing to ensure accuracy, fairness, and safety standards are met.
- Stay abreast of the latest advancements in AI research and integrate cutting-edge techniques into our production workflows.
- Lead technical mentorship for junior engineers and contribute to the company's technical documentation and standards.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related field, with 5+ years of industry experience in AI/ML.
- Deep expertise in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
- Strong understanding of transformer architectures, attention mechanisms, and pre-training methodologies.
- Experience with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of deploying large-scale models into production environments.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Experience in ethical AI development and bias mitigation is highly preferred.