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Tech Stack
Tools & technologiesCloudPyTorchTensorflow
About the role
Key responsibilities & impact- Research, design, develop, fine-tune, and evaluate LLMs, intelligent agents, and deep learning models for AI security and system optimisation.
- Create advanced tools for automated red-teaming, vulnerability detection, adversarial attack simulation, and intelligent defences.
- Implement and deploy AI/LLM-powered features as robust, scalable, and secure components within the client's security platforms.
- Optimise AI/LLM models for inference speed, cost-efficiency, and resource utilisation in production.
- Develop and maintain pipelines for end-to-end AI/LLM workflows covering data preprocessing, training, fine-tuning, validation, deployment, and monitoring.
- Proactively monitor model performance and behaviour in production, addressing safety, alignment, and security vulnerabilities.
- Apply software engineering best practices to AI/LLM workflows for secure and seamless deployments.
- Partner with cross-functional teams, including researchers and engineers, to productionise cutting-edge AI/LLM techniques and integrate them into the client's products.
- Translate technical advancements into practical solutions aligned with customer needs and business objectives.
- Communicate findings and solutions through documentation and presentations for data-driven decision-making.
- Develop tools and strategies to detect and mitigate vulnerabilities such as prompt injection and data poisoning threats.
- Investigate and utilise advancements in Reinforcement Learning from Human Feedback (RLHF) to improve AI model reliability and alignment.
- Continuously stay updated on industry trends and research to integrate state-of-the-art AI/LLM advancements into the platform.
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- 3-5 years of professional experience, including hands-on experience developing, training, and deploying AI/LLM models.
- Familiarity with AI/LLM libraries and tools such as PyTorch, TensorFlow, Hugging Face Transformers, or similar frameworks.
- Understanding of LLM security vulnerabilities and implementing mitigation solutions.
- Proven expertise in deploying AI/ML models in production environments, with emphasis on secure deployments.
- Strong analytical and problem-solving skills applied to complex AI challenges.
- Experience fine-tuning and deploying large-scale language models, including using frameworks like vLLM or TGI.
- Familiarity with Reinforcement Learning from Human Feedback (RLHF) for improving LLM alignment and performance.
- Knowledge of vector databases (e.g., Pinecone, Weaviate) and their applications in AI workflows.
- Expertise with cloud platforms and distributed computing for large-scale AI operations.
- Hands-on experience optimising models for cost, speed, and resource efficiency in production workflows.
Benefits
Comp & perks- Comprehensive Private Medical Coverage
- Support for Mental Health Expenses
- Life Insurance Options
- Attractive Compensation Package
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
LLMsdeep learningAI securityvulnerability detectionadversarial attack simulationdata preprocessingmodel fine-tuningmodel deploymentReinforcement Learning from Human Feedback (RLHF)optimising AI models
Soft Skills
analytical skillsproblem-solvingcommunicationcollaborationdocumentation
Certifications
Bachelor's degree in Computer ScienceMaster's degree in Artificial IntelligenceMaster's degree in Machine Learning