Testing & Validation: Conduct rigorous testing of AI systems in simulation and real-world environments to ensure reliability and accuracy. Documentation: Prepare comprehensive technical documentation, including model architectures, algorithms, and results for knowledge sharing and reference.
Bachelor’s or Master’s degree in Artificial Intelligence, Computer Science, Robotics, or any related field.
At least 5 years of experience working in AI, machine learning, or robotics engineering.
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Assess the commercial potential and technical readiness levels of internal R&D projects for potential spin-offs or new business incubation.
Bachelor's degree in Engineering (e.g., Electrical, Mechanical, Chemical, Software, Materials Science, Biomedical) or a relevant scientific discipline from an accredited institution.
Master's or Ph.D. in a relevant engineering or scientific field is highly preferred.
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Sentinel is working with a major global technology and construction provider to recruit an experienced AI Security Engineer to join a team at the forefront of securing enterprise AI solutions.
This is an exciting opportunity to work with emerging Generative AI and Large Language Model (LLM) technologies, evaluating cutting-edge security products, conducting hands-on technical PoCs, and helping shape the future adoption of AI security controls across a large-scale enterprise environment.
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Documentation: You will be responsible for documenting the workflows you create for future reference and for the rest of the team. Once again, even more boring, but use AI to make this easier!
Currently pursuing or recently completed a degree in Computer Science, Information Systems, Engineering, or a related technical field.
A strong foundational understanding of programming logic, data structures, and APIs. While you won't be writing extensive code, a technical mindset is crucial.
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Safety & Bias Auditing: Lead "Red Teaming" sessions to identify potential biases, toxic content, or PII (Personally Identifiable Information) leaks.
Regression Testing: Manage the evaluation of model performance across different versions to ensure seamless upgrades and stability.
Collaboration & Delivery: Work with Product Owner, Developers, AI Engineers and UX Designers to deliver the Virtual Companion. Contribute to sprint planning, code reviews and documentation.
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Monitor and support progress for projects with low to medium levels of complexity and ensure appropriate resource allocations and portfolio prioritization.
Implement, monitor and track audit control mechanisms within area of scope to track performance and provide actionable solutions that contribute to improvements.
Support more senior team members to assess instrumentation and automation engineering development and application opportunities and further dissemination across common practice areas.
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Deployment & Operationalization: Package, deploy, and operationalize machine learning models and LLM solutions, ensuring high availability, scalability, and optimal latency.
Model Optimization: Perform model fine-tuning, quantization, and optimization of model serving performance (latency/throughput) and inference costs.
Candidates should possess foundational knowledge in Computer Science and Software Development, including Artificial Intelligence, algorithms, data structures, and version control.
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