What youll be doing:
Developing agentic AI systems for security, combining generative models, RAG, and tool-augmented reasoning to automate threat analysis and response workflows.
Optimizing and fine-tuning models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.
Developing, implementing and improving models and algorithms across media types, whether time series, images, text, audio or video.
Leveraging data pipelines to efficiently process and transform large volumes of data for training and inference purposes.
Applying alignment techniques and parameter efficient fine-tuning to improve model performance.
Measuring and benchmarking model and application performance to drive improvements.
Driving the gathering, building, and annotation of domain specific datasets for benchmarking and training.
Collaborating closely with software and hardware engineers on new features and improvements. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.
What we need to see:
MS/PhD with expertise in Computer Science, Computer Engineering, Electrical Engineering or related field with a focus on Deep Learning or Machine Learning.
5+ years of experience in deep learning and machine learning in a production environment.
Excellent Python programming skills, strong software design fundamentals, and experience leveraging coding agents in development workflows.
Hands-on experience with deep learning development frameworks and libraries (e.g. TensorFlow, PyTorch).
Experience with large scale production systems and pipelines, with a track record of developing production-grade models
Experience with agentic AI systems, agent frameworks, and evaluation of agent performance and reliability.
Strong algorithm development experience, with knowledge of inference optimization techniques such as model distillation, quantization, pruning.
Background with algorithms including zero/few-shot learning, self-supervised and unsupervised learning and generative AI models for synthetic data creation.
Experience with fine-tune / training LLM models
You are proactive, take full ownership of your deliverables, have a can-do approach, and are excited to learn, explore and apply your skills and creativity to some of the most challenging and rewarding problems in the field.
What will make you stand out from the crowd:
Strong software development experience.
Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT.
Experience with tools for data processing and storage.
Security and networking background, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.
