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.