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- Lead efforts in large language models (LLMs), agentic AI, AI reasoning and planning, and complex state representation.
- Conduct research intersecting deep learning, analytics, reinforcement learning, complex networks, and graph theory.
- Develop and implement cutting-edge AI solutions leveraging state-of-the-art models and methodologies.
- Rapidly develop and deploy MVPs and AI capabilities with the data science team.
- Design, build, and optimize AI models for real-world applications.
- Apply advanced techniques in meta-learning, multi-task learning, in-context learning, and prompt engineering.
- Train, fine-tune, and evaluate models using industry-standard techniques.
- Optimize AI systems for performance, efficiency, and scalability.
- Implement rigorous benchmarking and performance monitoring for deployed models.
- Work directly with the Head of AI to achieve key milestones in the AI strategy.
- Guide and mentor junior AI engineers and data scientists.
- Collaborate with cross-functional teams to integrate AI solutions into broader business objectives.
- 8+ years of experience in AI engineering, deep learning, and applied research.
- Proven track record of leading AI-driven projects and innovation efforts.
- Experience developing and deploying AI models in production environments.
- Deep Learning & AI Frameworks: Proficiency in PyTorch, PyTorch Geometric, and Hugging Face.
- Graph Theory & Complex Networks: Experience with NetworkX, Graph Theory, and Complex Networks Theory.
- Neural Network Architectures: Strong knowledge of Transformers, LSTMs, RNNs, GNNs, Heterogeneous Graph Neural Networks (HGNNs).
- Reinforcement Learning & AI Reasoning: Expertise in reinforcement learning, agentic AI, and AI-driven decision-making.
- Model Engineering: Knowledge of meta-learning, multi-task learning, in-context learning, prompt engineering, model training, model optimization, and model evaluation.
- Ability to bridge the gap between AI research and business needs.
- Strong problem-solving and critical-thinking skills.
- Experience in designing and deploying AI solutions in cybersecurity, finance, or other high-stakes industries.
- Lead efforts in large language models (LLMs), agentic AI, AI reasoning and planning, and complex state representation.
- Conduct research intersecting deep learning, analytics, reinforcement learning, complex networks, and graph theory.
- Develop and implement cutting-edge AI solutions leveraging state-of-the-art models and methodologies.
- Rapidly develop and deploy MVPs and AI capabilities with the data science team.
- Design, build, and optimize AI models for real-world applications.
- Apply advanced techniques in meta-learning, multi-task learning, in-context learning, and prompt engineering.
- Train, fine-tune, and evaluate models using industry-standard techniques.
- Optimize AI systems for performance, efficiency, and scalability.
- Implement rigorous benchmarking and performance monitoring for deployed models.
- Work directly with the Head of AI to achieve key milestones in the AI strategy.
- Guide and mentor junior AI engineers and data scientists.
- Collaborate with cross-functional teams to integrate AI solutions into broader business objectives.
- 8+ years of experience in AI engineering, deep learning, and applied research.
- Proven track record of leading AI-driven projects and innovation efforts.
- Experience developing and deploying AI models in production environments.
- Deep Learning & AI Frameworks: Proficiency in PyTorch, PyTorch Geometric, and Hugging Face.
- Graph Theory & Complex Networks: Experience with NetworkX, Graph Theory, and Complex Networks Theory.
- Neural Network Architectures: Strong knowledge of Transformers, LSTMs, RNNs, GNNs, Heterogeneous Graph Neural Networks (HGNNs).
- Reinforcement Learning & AI Reasoning: Expertise in reinforcement learning, agentic AI, and AI-driven decision-making.
- Model Engineering: Knowledge of meta-learning, multi-task learning, in-context learning, prompt engineering, model training, model optimization, and model evaluation.
- Ability to bridge the gap between AI research and business needs.
- Strong problem-solving and critical-thinking skills.
- Experience in designing and deploying AI solutions in cybersecurity, finance, or other high-stakes industries.