Lead Engineer - ML QA

Client of Talentmate

Overview

As the Lead Engineer - ML QA at Inception, you will be responsible for ensuring the quality and reliability of machine learning models and systems through comprehensive testing and validation processes. Reporting to the Director - Factory Engineering, you will lead the efforts in developing and implementing QA strategies specifically tailored for machine learning projects. Your expertise in quality assurance and machine learning will be crucial in maintaining high standards and driving the successful deployment of ML models.

The opportunity

The Lead Engineer - ML QA is a senior technical role focused on validating the performance, accuracy, and robustness of machine learning models. This role involves working closely with data scientists, software engineers, and product teams to establish QA processes and ensure that ML models meet the required standards. The ideal candidate will have extensive experience in QA for machine learning projects, a strong background in ML techniques, and a proven ability to manage QA initiatives.

Inception is the UAE s national-scale enabler in AI Research and Development. Partnering with Microsofts AI SaaS, we offer domain-specific Agentic AI Orchestrator platforms utilizing reasoning agents for precise and cost-effective services. Our focus includes AI incubation, IP creation, applied AI R&D, and AI investment products. By creating models tailored to specific domains and languages, we ensure superior accuracy and efficiency. Collaborating with top universities and industry giants to drive significant advancements in AI technology within the region.

Responsibilities

As the Lead Engineer - ML QA, you will be responsible for managing the QA processes for machine learning models to ensure they meet the company s quality standards. Your role will encompass a range of activities focused on QA strategy, model validation, and cross-functional collaboration.

• QA Strategy and Implementation:
• Develop and implement a comprehensive QA strategy for machine learning models and systems.
• Lead the creation and execution of QA plans, test cases, and validation processes.
• Ensure QA processes align with business goals and strategic objectives.
• Model Validation:
• Design and conduct tests to validate the accuracy, performance, and robustness of ML models.
• Implement automated testing frameworks and tools for continuous model validation.
• Analyze test results and provide actionable feedback to data science and engineering teams.
• Performance and Reliability Testing:
• Conduct performance testing to ensure ML models can handle production workloads.
• Implement reliability testing to assess model behavior under different conditions.
• Identify and mitigate potential risks and issues related to model performance and reliability.
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Qualifications

Skills and attributes for success

• Bachelor s degree in Computer Science, Engineering, Machine Learning, or a related field is required. A Master s degree is preferred.
• Minimum of 10 years of experience in quality assurance, with a focus on machine learning or data-driven projects.
• Proven track record of implementing and managing QA practices in a complex environment.
• Experience with QA tools and technologies (e.g., automated testing frameworks, performance testing tools).
• To qualify for the role you must have
• Proficiency in machine learning frameworks and libraries.
• Excellent problem-solving and analytical skills.
• Ability to lead and mentor technical teams.
  
  

Tech stack

• Programming languages: Python, Java, SQL
• Testing Tools: Selenium, JUnit, PyTest, Gatling, Postman
• CI/CD: Jenkins, GitLab CI/CD,
• Version Control: Git
• Cloud Services: Azure (optional AWS, GCP)
• Databases: MySQL, MongoDB, PostgreSQL
• Monitoring Tools: Grafana, Kibana
• Collaboration: Jira, Confluence
• Other Technologies: Docker, Kubernetes
  
  

What We Look For

If you are a performance-driven, inquisitive mind with the agility to adapt to ambiguity, you will fit right in. You should be eager to explore opportunities to build meaningful collaborations with stakeholders and aspire to create unique customer-centric solutions. Bias for action and a passion to conquer new frontiers in the AI space is at the heart of the Inception community.