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Niche hybrid AI, quantum and HPC specialization reduces candidate pool despite metro location.
Highly domain-specific hybrid optimisation, HPC and quantum skills limit cross-industry transferability.
Specialized ML, optimisation, quantum and HPC expertise required, creating strict technical filters.
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Design and develop hybrid AI-optimization techniques combining machine learning, metaheuristics, and exact solvers to enable efficient, scalable AI solutions and end-to-end intelligent decision-making.
Collaborate with Quantum, AI, and HPC R&D teams to create PoC studies comparing classical and hybrid quantum/AI optimization approaches and integrate HPC-accelerated AI solvers for large-scale combinatorial problems.
Develop AI-powered decision support systems for industries like finance, logistics, and cybersecurity, advise clients on hybrid AI-optimization adoption, and contribute to thought leadership through papers and patents.
Experience designing and developing AI-driven optimization techniques incorporating machine learning and metaheuristics.
Proven ability to collaborate cross-functionally with R&D and software engineering teams for integration and deployment of AI optimization models.
Work Experience Required: Not explicitly mentioned in the JD.
Knowledge of secure software engineering principles, agile software development, and familiarity with MLOps best practices for AI model deployment.
Technical professional capable of applying hybrid AI-optimization methods to complex, large-scale combinatorial problems leveraging HPC and quantum computing interfaces.
Experience working in cross-disciplinary teams integrating AI, optimization, quantum computing, and software engineering for production-grade solutions.
Candidate who can engage with clients in advisory capacity on advanced AI-optimization strategies and contribute to research outputs like white papers and patents.