I am a dedicated machine learning engineer with a passion for implementing and optimizing machine learning systems across various platforms like AWS, GCP, and Azure. My expertise includes developing conversational agents, fact-checking systems, and language learning tools utilizing LLMs, along with strong skills in MLOps practices such as CI/CD and model lifecycle management. I have a robust foundation in designing scalable architectures and integrating retrieval systems to enhance precision and accuracy. My commitment to AI safety and alignment research helps me stay ahead in this rapidly evolving field, always looking for new ways to transform research insights into real-world applications.
β’ Facilitating cross-functional collaboration between ML, software engineering, and product teams to translate research into production.
β’ Investigating new research trends in LLM alignment, self-supervised learning, and reinforcement learning for product innovation.
β’ Defining and enforcing MLOps best practices (CI/CD, model versioning, monitoring) to streamline model lifecycle management.
β’ Architecting scalable ML systems for efficient inference, fine-tuning, and retraining.
β’ Optimizing LLM inference pipelines for low-latency, high-throughput applications.
β’ Red-teaming LLMs via jailbreaking, prompt injection, and other Prompt Hacking techniques
β’ Designing and implementing guardrails against adversarial attacks that target the vulnerabilities of SOTA LLMs
β’ Staying on top of the rapidly-growing field of AI Safety research
β’ Developing performance evaluation tools for LLM benchmarking
β’ Deploying training, evaluation, and testing pipelines in production
β’ Designing effective AI solutions based on thorough literature reviews
β’ Deploying large-scale training and evaluation pipelines for the implemented solutions
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