Solution Director

Job Title:

AI Research & Innovation – AI researcher

Job Family:
Program/Delivery/Capability Management

No. of Positions: 2

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.

• 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.

• 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, Tensor Flow) 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, Sage Maker, 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.

• 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.