We are seeking an Agentic AI Engineer to join our growing AI & Data Science team. This role focuses on building, deploying, and scaling agentic AI systems—autonomous, goal-driven AI agents that augment decision-making, automate complex workflows, and deliver actionable insights across Exiger’s risk and compliance platforms. You will work closely with data scientists, software engineers, and product leaders to push the boundaries of applied AI in high-stakes environments.

PROFESSIONAL EXPERIENCE REQUIRED:

• Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field (PhD preferred but not required).
• Strong programming skills in Python and experience with AI/ML libraries such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex.
• Hands-on experience with LLMs, RAG pipelines, prompt engineering, and fine-tuning.
• Knowledge of multi-agent systems, reinforcement learning, or autonomous planning frameworks.
• Experience with cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).
• Familiarity with compliance, risk management, or supply chain domains is a plus.
• Excellent problem-solving and communication skills, with the ability to work in cross-functional teams.

• Design, develop, and deploy agentic AI systems capable of reasoning, planning, and autonomously executing multi-step tasks.
• Integrate large language models (LLMs), vector databases, and external APIs into dynamic agent frameworks.
• Collaborate with product teams to translate compliance, risk, and supply chain use cases into scalable AI-driven workflows.
• Implement memory, retrieval, and tool-use mechanisms for persistent, context-aware agents.
• Optimize performance, latency, and cost trade-offs in large-scale AI applications.
• Contribute to Exiger’s innovation in safe, reliable, and interpretable AI—ensuring outputs align with compliance, regulatory, and ethical standards.
• Stay on the cutting edge of AI research, particularly in agentic architectures, reinforcement learning, and multi-agent systems.