Job Description
We are looking for a visionary Senior AI Engineer to lead the next generation of artificial intelligence solutions at Apex Innovations. As we bridge the gap between theoretical research and real-world application, we need a technical leader who can architect scalable systems and mentor high-performing engineering teams.
In this role, you will be at the forefront of Generative AI and Large Language Models (LLMs), pushing the boundaries of what's possible in automation and creative computing. If you are passionate about building systems that think and want to define the future of technology, we want to hear from you.
Why Join Apex Innovations?
- Industry Leadership: Work on cutting-edge projects that redefine industry standards.
- Competitive Compensation: A generous salary package plus equity options.
- Flexible Culture: Embrace a remote-first culture with a focus on work-life balance.
- Continuous Learning: Access to the latest tools, conferences, and a dedicated learning budget.
Responsibilities
- Design, develop, and deploy state-of-the-art deep learning models, with a focus on Generative AI and NLP.
- Optimize model inference latency and accuracy to ensure seamless user experiences at scale.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers.
- Build robust MLOps pipelines for model training, validation, and deployment using cloud infrastructure.
- Stay abreast of the latest research in the AI field and adapt best practices to our production environment.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- PhD or Master's degree in Computer Science, Mathematics, or a related technical field.
- 5+ years of professional experience in software engineering and machine learning.
- Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Extensive experience with transformer architectures and Large Language Models (e.g., GPT, BERT).
- Familiarity with MLOps tools, containerization (Docker/Kubernetes), and cloud platforms (AWS, GCP, or Azure).
- Experience with vector databases (Pinecone, Milvus) and RAG architectures.