Job Title: AI Research & Innovation – AI researcher Job Family: Program/Delivery/Capability Management No. of Positions: 2 Job Description (Posting) About the Role: We are seeking an experienced Applied AI Research & Engineering Researcher to lead the technical foundation and innovation within our AI division. This leader will define the core ML/AI research agenda, oversee the development of novel algorithms and models, and drive their robust deployment into production. The role is hands‑on technical leadership for someone who excels at managing a team of researchers and translating complex academic concepts into scalable, reliable enterprise solutions. Responsibilities

Technical Vision & Research Strategy: Define the multi‑year research and engineering roadmap, lead deep‑learning and generative AI model development, and translate academic research into production. MLOps & Production Engineering: Establish MLOps best practices, enforce CI/CD, ensure scalable, low‑latency AI systems, and drive resource optimisation. Team Leadership & Technical Mentorship: Recruit, mentor, and manage a high‑performing team of Applied AI Scientists and ML Engineers, foster a research‑driven culture, and maintain code quality.

Qualifications and Competencies

15+ years hands‑on experience in Machine Learning, Deep Learning, or AI Research with focus on building and deploying complex models. 5+ years technical leadership managing Data Scientists and ML Engineers. Expert‑level proficiency in core ML frameworks (PyTorch, TensorFlow) and data science languages (Python/R). Experience building a commercial practice or product focused on Generative AI and Large Language Models (LLMs). Demonstrated expertise in at least two major AI domains (Deep Learning, NLP, Computer Vision). Proven track record translating technical output into commercial results. Deep practical knowledge of MLOps principles and experience with cloud‑native ML services (Google Cloud Vertex AI, SageMaker, Azure ML). Ph.D. or Master’s degree in Computer Science, Machine Learning, or a highly quantitative field, or equivalent demonstrated technical leadership experience. Bachelor of Technology/Engineering.

Technical / Functional Skills

Strong portfolio of research publications (NeurIPS, ICML, KDD) or patents related to applied AI. Extensive experience with distributed computing frameworks (Spark, Ray) for large‑scale model training and inference.

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