We are looking for an AI Research Lead to join its AI-OPS project and a successful software team as part of the new and rapidly evolving project. If you're passionate about driving cutting-edge AI innovation while coordinating with multidisciplinary R&D teams, we'd love to hear from you.
Responsibilities:
Set the technical vision and roadmap for AI research initiatives aligned with organizational goals. Oversee the full lifecycle of AI model development, from ideation to deployment.
Mentoring researchers/scientists, fostering collaboration and professional growth. Conduct performance reviews and resolve technical blockers.
Finetune novel LLM algorithms for domain-specific applications.
Evaluate emerging AI trends and recommend adoption pathways.
Oversee and contribute to developing novel classical AI algorithms and techniques to enhance machine learning results.
Work collaboratively with cross-functional teams, including Software engineers, DevOps, Network Architects, and Product Managers, to align development with business goals.
Ensure research outcomes are practical, scalable, and can be transitioned into production.
Represent the AI team in technical discussions, stakeholder meetings, and conferences.
Requirements: Requirements:
PhD holders: At least 3+ years of experience in AI/ML research & development.
Masters holders: At least 5+ years of experience in AI/ML research & development.
Leading: 1+ years in team leading.
Strong background in machine learning, deep learning, and AI algorithms.
Hands-on experience with deep learning frameworks such as PyTorch and TensorFlow, as well as libraries like Hugging Face Transformers.
Proficiency in Python, C++, or other relevant programming languages.
Experience in GPU acceleration (e.g., CUDA, Triton, TensorRT, vLLM) and scalable AI model fine-tuning.
Nice to have:
PhD in AI, Computer Science, Engineering, or related field
Published research in AI/ML conferences
Experience with AI APIs, prompt engineering, Multi-Agent architectures, retrieval-augmented generation (RAG), LoRA, or AI-Ops
Experience with data stream processing pipelines and data analytics
Knowledge of Docker and Kubernetes for containerization and orchestration
Familiarity with CI/CD pipelines and MLOps tools such as Jenkins, GitHub Actions, GitLab CI, or MLflow for model deployment and monitoring
Experience with computer networks (e.g., CCNA/CCNP level)
This position is open to all candidates.