KoiReader - Lead AI Engineer

We are looking for a Lead AI Engineer who can bridge the gap between "Research" and "Reality." You won't just be importing libraries here; you will be architecting the intelligence engine that powers our Industrial AI platform. You will build high-performance Multimodal Systems (Vision + Language) that run efficiently on both Edge devices and the Cloud to solve complex logic problems for Fortune 500 supply chains.

Key Responsibilities :

- Rapid Prototyping & Innovation : Don't just use open-source codeinnovate on top of it. Experiment with the latest libraries to build novel solutions for complex logistics challenges.

- End-to-End Ownership : Translate abstract business requirements into logical, scalable AI architectures. You will own the solution from data analysis to model deployment.

- Performance Engineering : Its not enough to be accurate; it must be fast. Diagnose, troubleshoot, and optimize inference pipelines for low latency and high throughput.

- Data-Centric AI : Drive the strategy for Data Analysis, Feature Engineering, and Augmentation. You will guide the annotation team and build pipelines to extract meaningful insights from massive Vision and Text datasets.

- Advanced Vision & NLP : Build robust solutions for Person/Scene understanding (Pose Estimation, Re-Identification) and integrate GenAI/LLM capabilities to add semantic understanding to visual data.

- Cross-Functional Collaboration : Work closely with the DevOps and Product teams to translate AI needs into effective, fault-tolerant technical solutions.

- Technical Leadership : Experience leading AI/Computer Vision projects, making architecture decisions, conducting code reviews, and mentoring engineers to deliver production-ready solutions.

- Engineering Management : Experience managing technical teams, allocating resources, setting development priorities, conducting performance reviews, and supporting career growth.

Skills & Requirements :

- Production Python : Strong experience writing clean, modular, and fault-tolerant code. You understand that a model in a notebook is not a product.

- Deep Learning Stack : Proficiency in PyTorch is essential and experience with inference optimization tools like TensorRT is also required. Experience with practical edge deployment is a massive plus.

- Custom Model Training : Familiarity with training or fine-tuning custom AI models (Detectors, Classifiers) from scratch.

- Computer Vision Mastery : Deep understanding of Image Processing technologies (OpenCV, Dlib, NumPy) and modern architectures (YOLO, ResNet, etc.), OCRs and VLMs.

- NLP & GenAI : Hands-on experience with Hugging Face, LangChain, and NLP libraries (Spacy, NLTK). Ability to implement RAG pipelines or Agentic workflows.

- Complex Vision Tasks : Experience with advanced problems like Person Re-Identification, Pose Estimation, and Tracking.

- Applied AI : A proven track record of successfully applying machine learning to solve real-world problems (not just Kaggle competitions).

- Team Management : Experience managing and growing high-performing engineering teams, conducting code reviews, defining development processes, and fostering a culture of engineering excellence.

Brownie Points :

- Cutting-Edge Tech : Knowledge of the latest advancements in AI, especially Vision Transformers (ViTs), CLIP, and Multimodal LLMs.

- DevOps Awareness : Understanding of Docker and Git. You know how to containerize your application for deployment.

- Cloud/Edge : Experience deploying models on AWS or NVIDIA Jetson devices.

What We Offer :

- Meritocracy : A candid startup culture where the best ideas win.

- The Playground : Access to the latest NVIDIA Hardware and cutting-edge Generative AI tools.

- Ownership : Work with a performance-oriented team driven by autonomy and open to experiments.

- Impact : Design systems for high accuracy and scalability that physically move the global supply chain.

(ref:hirist.tech)