Principal engineer, applied ai & fluid dynamics

Role Overview: You will be working with HEN Technologies, a deep-tech company that focuses on building an intelligent fire suppression ecosystem using AI, Io T, and advanced fluid dynamics. As a senior AI/Machine Learning engineer, you will lead the design and implementation of advanced ML systems across various areas such as physics-driven modeling, edge inference, and cloud-scale intelligence. This role requires expertise in machine learning, applied physics, and fluid dynamics to develop production-grade AI systems. Key Responsibilities: - Partner with Computational Fluid Dynamics engineer to design and implement Physics-Informed Neural Networks (PINNs) and hybrid physics-ML models. - Translate first-principles physics into scalable ML architectures for fluid flow, fire dynamics, and suppression behavior. - Validate models against simulation and real-world sensor data. - Architect, build, and deploy low-latency ML inference pipelines on edge devices like NVIDIA Jetson for real-time and resource-constrained conditions. - Develop descriptive, predictive, and prescriptive models and design cloud-based inference, analytics, and decision systems. - Build and integrate Retrieval-Augmented Generation (RAG) pipelines for contextual reasoning, diagnostics, and operational intelligence. - Design real-time and batch Io T data pipelines for ingesting, cleaning, and storing large-scale telemetry. - Own ML lifecycle automation including training, evaluation, deployment, monitoring, and retraining. - Apply advanced techniques like time-series modeling, deep learning, and RL in real-world environments. Qualifications Required: - Masters or Ph.D. in Computer Science, Applied Mathematics, Physics, or a related field. - 12+ years of hands-on experience in machine learning, applied AI, or data engineering with technical leadership. - Proficiency in Python and ML-centric development with experience in Py Torch and/or Tensor Flow. - Understanding of fluid dynamics, PDEs, and physical modeling. - Experience with cloud platforms like AWS, GCP, or Azure. - Expertise in data pipelines and streaming systems such as Apache Pulsar + Flink, Kafka, Spark, Airflow, MQTT. - Experience working alongside CFD or simulation teams is desirable. - Experience deploying ML on edge hardware, preferably NVIDIA Jetson or similar. - Experience with RAG systems, vector databases, and LLM integration. - Experience in safety-critical or real-time systems. - Familiarity with Docker, Kubernetes, and production ML systems. Role Overview: You will be working with HEN Technologies, a deep-tech company that focuses on building an intelligent fire suppression ecosystem using AI, Io T, and advanced fluid dynamics. As a senior AI/Machine Learning engineer, you will lead the design and implementation of advanced ML systems across various areas such as physics-driven modeling, edge inference, and cloud-scale intelligence. This role requires expertise in machine learning, applied physics, and fluid dynamics to develop production-grade AI systems. Key Responsibilities: - Partner with Computational Fluid Dynamics engineer to design and implement Physics-Informed Neural Networks (PINNs) and hybrid physics-ML models. - Translate first-principles physics into scalable ML architectures for fluid flow, fire dynamics, and suppression behavior. - Validate models against simulation and real-world sensor data. - Architect, build, and deploy low-latency ML inference pipelines on edge devices like NVIDIA Jetson for real-time and resource-constrained conditions. - Develop descriptive, predictive, and prescriptive models and design cloud-based inference, analytics, and decision systems. - Build and integrate Retrieval-Augmented Generation (RAG) pipelines for contextual reasoning, diagnostics, and operational intelligence. - Design real-time and batch Io T data pipelines for ingesting, cleaning, and storing large-scale telemetry. - Own ML lifecycle automation including training, evaluation, deployment, monitoring, and retraining. - Apply advanced techniques like time-series modeling, deep learning, and RL in real-world environments. Qualifications Required: - Masters or Ph.D. in Computer Science, Applied Mathematics, Physics, or a related field. - 12+ years of hands-on experience in machine learning, applied AI, or data engineering with technical leadership. - Proficiency in Python and ML-centric development with experience in Py Torch and/or Tensor Flow. - Understanding of fluid dynamics, PDEs, and physical modeling. - Experience with cloud platforms like AWS, GCP, or Azure. - Expertise in data pipelines and streaming systems such as Apache Pulsar + Flink, Kafka, Spark, Airflow, MQTT. - Experience working alongside CFD or simulation teams is desirable. - Experience deploying ML on edge hardware, preferably NVIDIA Jetson or similar. - Experience with RAG systems, vector databases, and LLM integration. - Experience in safety-critical or real-time systems. - Familiarity with