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As a Rapid QA Engineer at [Company Name], you will be instrumental in ensuring the rapid delivery of high-quality software products. You will be responsible for quickly and efficiently identifying, documenting, and resolving issues, playing a critical role in our agile development lifecycle.

Responsibilities

• Design, develop, and execute rapid test plans and test cases for new features and existing functionalities.
• Perform various types of testing, including functional, regression, integration, and performance testing, with a focus on speed.
• Identify, document, and track defects, working closely with development teams for quick resolution.
• Collaborate with product managers and engineers to understand requirements and provide timely feedback.
• Contribute to the continuous improvement of our QA processes and automation strategies to enhance testing efficiency.
• Ensure that all software releases meet rigorous quality standards in a fast-paced environment.

Qualifications

• 3-5 years of professional experience in Quality Assurance or a similar role.
• Proven ability to work in a rapid development cycle with tight deadlines.
• Strong understanding of software QA methodologies, tools, and processes.
• Experience with Agile/Scrum development methodologies.
• Familiarity with test management and defect tracking tools (e.g., Jira, Azure DevOps).
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills.

Preferred Qualifications

• Experience with automated testing frameworks and tools (e.g., Selenium, Playwright, Cypress).
• Proficiency in scripting languages (e.g., Python, JavaScript) for test automation.
• Experience testing APIs or microservices.

TalentProof Demo is seeking an exceptional Senior Principal Lead Solutions Architect and Technical Program Manager to drive the design, development, and delivery of advanced AI/ML solutions across multiple business units. This pivotal role requires a deep technical background in AI/ML, strong architectural design skills, and proven experience in managing complex technical programs from inception to deployment. You will be responsible for bridging the gap between cutting-edge research, scalable engineering, and strategic business objectives.

Responsibilities

• Lead the architectural definition, design, and implementation strategies for enterprise-level AI/ML platforms and applications.
• Drive cross-functional technical programs, managing timelines, resources, and deliverables for AI/ML projects.
• Collaborate with data scientists, machine learning engineers, and product teams to translate business requirements into robust, scalable, and secure technical solutions.
• Provide technical leadership and mentorship to engineering teams, ensuring best practices in software development, MLOps, and system reliability.
• Evaluate and recommend new technologies, tools, and methodologies to enhance our AI/ML capabilities and infrastructure.
• Identify and mitigate technical risks and challenges throughout the project lifecycle.
• Communicate complex technical concepts and project statuses effectively to both technical and non-technical stakeholders.

Qualifications

Required Skills & Experience:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• 8+ years of experience in software engineering, with at least 5 years focused on AI/ML solution architecture, design, and development.
• Proven experience in a lead architect or technical program manager role for large-scale AI/ML initiatives.
• Deep expertise in machine learning principles, algorithms, and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of cloud platforms (AWS, Azure, GCP) and experience with MLOps tools and practices (e.g., Kubernetes, Docker, MLflow, Kubeflow).
• Exceptional problem-solving skills and the ability to design scalable, high-performance systems.
• Excellent communication, interpersonal, and leadership skills.

Preferred Qualifications:

• Ph.D. in a relevant field.
• Experience with distributed computing frameworks (e.g., Spark, Hadoop).
• Familiarity with data governance, privacy, and security best practices in AI/ML.
• Publications or active contributions to the AI/ML community.