Applied AI Engineer II

Role Overview

• 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.
• 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.
• Develop advanced-scope tools to automate research and development processes and enhance workflow efficiency.
• Identify novel solutions and take an active role in designing modular application units.
• 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).

Requirements

• 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.
• 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.

Tech Stack

• Cloud
• Python

Benefits

• Health insurance
• Work-life balance
• Collaborative environment