Lead AI Engineer

Overview

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 code—innovate 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: It’s 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 deployement 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.