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