Everseen: A leader in vision AI solutions for the world’s leading retailers.
The Role
As an Applied AI Engineer – Edge AI & Computer Vision, you will play a pivotal role in developing intelligent systems that operate efficiently on edge devices. You will design, refine, and own robust Machine Learning (ML) and Computer Vision (CV) systems, with a direct focus on edge deployment, automated pipelines, performance optimization, and seamless software integration.
This role bridges the gap between cutting-edge research and real-world deployment, refactoring and hardening AI models to meet strict latency, scalability, and resource constraints on specialized edge hardware. The successful candidate will act as a key contributor to the team’s technical strategy and support the long-term production lifecycle of graduated AI products.
Technology Stack
As an Applied AI Engineer at Everseen, you’ll have the opportunity to work with and develop your skills across a modern, high-performance tech stack:
Programming & Scripting: Python (primary foundation for our research, model prototyping, and development) and Bash scripting.
Deep Learning & Machine Learning: PyTorch (core neural network framework), ONNX, TensorRT for training, evaluation, and production deployment.
Computer Vision & Video Processing: OpenCV and custom image processing libraries to fuel our real-time video analysis and visual inspection algorithms.
Edge Deployment & Containerization: Docker for containerizing and executing low-latency inference and logic at the retail edge.
Cloud Infrastructure & CI/CD: Microsoft Azure and GitLab CI/CD for scaling model training, managing cloud storage, and automation.
What you’ll do
System Ownership & Delivery
Own and deliver key Machine Learning and Computer Vision components or features of a project.
Design, execute, and deliver robust applications that interface with edge devices.
Support graduated AI solutions throughout their production lifecycle, ensuring continuous reliability.
Optimization & Refactoring
Refine, refactor, and harden existing AI implementations to meet high-quality production standards.
Design and implement targeted initiatives to optimize system efficiency, real-time performance, and pipeline output.
Apply model optimization techniques (e.g., quantization, pruning, and latency tuning) for specialized, resource-constrained edge devices.
Tooling & Infrastructure
Develop advanced-scope tools to automate research and development processes and enhance workflow efficiency.
Manage production infrastructure for model training and serving, incorporating modern MLOps workflows and pipelines.
R&D Translation
Identify novel solutions and take an active role in designing modular application units.
Expand on experimental results presented by research teams and successfully transition them from research to robust production environments.
Cross-Functional Collaboration
Data Engineers: Ensure seamless integration between data pipelines and ML workflows; collaborate on feature engineering, format specification, and data validation.
DevOps: Support infrastructure scalability and reliability for ML projects; adhere to performance standards for ML services (observability, security, logging, and alerting).
Product Managers: Align on project timelines and deliverables; prioritize platform capabilities based on product needs and explore “the art of the possible.”
Software Engineers: Align on APIs and microservice protocols; coordinate to ensure optimal resource usage.
Collaborating with
Data Engineers: Ensure seamless integration between data pipelines and ML workflows; collaborate on feature engineering, format specification, and data validation; leverage data orchestration tools.
DevOps: Support infrastructure scalability and reliability for ML projects; adhere to performance standards for ML services (observability, security, logging, and alerting).
Product Managers: Align on project timelines and deliverables; prioritize platform capabilities based on product needs and explore “the art of the possible.”
Product Owners and Technical Writers: Align on technical requirements; collaborate to define user-friendly release notes.
Software Engineers: Align on APIs and microservice protocols; coordinate to ensure optimal resource usage.
Profile and Skills
Ideally 2–3 years of practical experience working on Machine Learning and Computer Vision systems deployed at the edge.
Proven experience in moving machines learning models successfully from research environments to high-performance, real-time production environments.
Bachelor’s degree in computer science, Engineering, Machine Learning, Mathematics, or a closely related technical field.
Master’s degree in a related technical domain is highly preferred.
Exceptional practical problem-solving and algorithmic skills. Highly analytical and able to identify trends, make data-driven decisions, and think critically to construct efficient solutions.
Proactive and highly adaptable; comfortable navigating ambiguity in a fast-paced, rapidly evolving ML environment.
Strong communication and teamwork skills; capable of aligning technical consensus and influencing peers with technical expertise while beginning to act as a mentor.
Technical and Engineering skills
Deep understanding of ML algorithms, tuning, training, and evaluation procedures, combined with a strong grasp of classical computer vision concepts.
Hands-on experience with hardware/resource optimization, deploying models on specialized/embedded edge devices, and optimization of real-time systems.
Solid understanding of software engineering principles, version control (Git), and CI/CD pipelines. Ability to integrate and maintain strict standards of code and model quality for long-term maintenance.
Strong proficiency in Python OR expert-level proficiency in a low-level/systems programming language (e.g., C/C++) with a willingness to learn Python.
Working knowledge of video streaming, processing, decoding, and format handling. Familiarity with major cloud platforms and data orchestration workflows.
