Technical Director - AI/MLE

Location: Bangalore, IndiaEmployment Type: Full-time About HEN Technologies HEN Technologies is a deep-tech company building the world’s first end-to-end intelligent fire suppression ecosystem, powered by AI, IoT, and advanced fluid dynamics. The company’s mission is to modernize how the world prevents and responds to fires by replacing outdated, manual systems with connected, data-driven technology that is more water-efficient and intelligently coordinated across every layer of the fireground. Backed by funding from Venture Capital investors, HEN Technologies is proving that the future of fire defense is intelligent, connected, and preventative.More than a hardware company, HEN Technologies is building the digital backbone for global fire defense - unlocking real-time data, AI-driven insights, and system-wide coordination that will define the next generation of emergency response.About the RoleWe are hiring a senior AI / Machine Learning engineer to lead the design and implementation of advanced ML systems across physics-driven modeling, edge inference, and cloud-scale intelligence.This role is ideal for a senior engineer who has deep hands-on expertise and is fluent in machine learning, applied physics, and fluid dynamics. You will collaborate closely with CFD engineer to develop Physics-Informed Neural Networks (PINNs) and develop production-grade AI systems spanning edge devices and cloud-based inference platforms, including RAG-based systems.You will own core AI/ML technical decisions end-to-end from research and modeling to deployment in safety-critical, real-time environments.ResponsibilitiesPartner with Computational Fluid Dynamics engineer to design and implement Physics-Informed Neural Networks (PINNs) and hybrid physics-ML models.Translate first-principles physics (fluid flow, fire dynamics, suppression behavior) into scalable ML architectures.Validate models against simulation and real-world sensor data.Architect, build and deploy low-latency ML inference pipelines on edge devices (e.g., NVIDIA Jetson) under real-time and resource-constrained conditions.Develop descriptive, predictive and prescriptive models. Design cloud-based inference, analytics, and decision systems to support fleet-wide intelligence.Build and integrate Retrieval-Augmented Generation (RAG) pipelines for contextual reasoning, diagnostics, and operational intelligence.Design real-time and batch IoT data pipelines for ingesting, cleaning, and storing large-scale telemetry.Own ML lifecycle automation: training, evaluation, deployment, monitoring, and retraining.Apply advanced techniques including time-series modeling, deep learning, and RL in real-world, noisy environments.RequirementsMaster’s, or PhD 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 demonstrated technical leadership.Strong proficiency in Python and ML-centric development with deep experience with PyTorch and/or TensorFlow.Strong understanding of fluid dynamics, PDEs, and physical modeling.Proven experience or strong interest in Physics-Informed Neural Networks (PINNs) or hybrid physics-ML approaches.Strong experience with cloud platforms (AWS, GCP, or Azure).Expertise in data pipelines and streaming systems (Apache Pulsar + Flink, Kafka, Spark, Airflow, MQTT).Experience working alongside CFD or simulation teams is highly 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.Experience with Docker, Kubernetes, and production ML systems.

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