Job Description
Join the Future of Intelligence.
We are Apex Innovations, a pioneer in next-generation artificial intelligence. As we prepare to define the technological landscape of 2026 and beyond, we are seeking a visionary Senior AI Architect to lead our research and engineering teams. You will be instrumental in building the core infrastructure for our proprietary Generative AI models, ensuring they are scalable, ethical, and capable of transforming industries.
In this role, you won't just maintain systems; you will architect the future. You will bridge the gap between theoretical research and production-grade deployment, working alongside world-class engineers and data scientists.
Responsibilities
- Architect & Deploy: Design and implement scalable AI infrastructure for Large Language Models (LLMs) and multimodal systems, ensuring high availability and performance.
- R&D Leadership: Lead cutting-edge research initiatives to push the boundaries of what's possible in AI by 2026.
- Model Optimization: Fine-tune and optimize existing models for latency, throughput, and cost efficiency using techniques like quantization and pruning.
- Collaboration: Partner with product managers and engineering teams to translate complex AI capabilities into user-centric features.
- Ethical AI: Establish and enforce guidelines for AI safety, fairness, and transparency in model training and deployment.
- Mentorship: Guide junior engineers and researchers, fostering a culture of innovation and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in software engineering or machine learning, with at least 2 years in a leadership or senior architect role.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong experience with cloud platforms (AWS, GCP, or Azure).
- Modeling: Deep understanding of Deep Learning architectures, NLP, and Generative AI concepts.
- Tools: Experience with MLOps tools (Docker, Kubernetes, MLflow) and version control (Git).
- Communication: Excellent verbal and written communication skills; ability to explain complex technical concepts to non-technical stakeholders.