AI/ML Engineer

Role Overview

• 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

• 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.

Tech Stack

• Cloud
• PyTorch
• Tensorflow

Benefits

• Comprehensive Private Medical Coverage
• Support for Mental Health Expenses
• Life Insurance Options
• Attractive Compensation Package