Software Quality Assurance Specialist (WallGold)

Job Description

We are looking for an AI Workflow Developer to join our R&D team. This role is designed for a technically skilled, hands-on professional who thrives at the intersection of research, prototyping, and implementation. You will monitor emerging AI trends, rapidly explore new tools and frameworks, and develop Minimum Viable Agents (MVAs) to test and demonstrate business use cases.


You will work closely with the R&D Team Lead and other departments to identify opportunities, build prototypes, and stack AI capabilities into workflows that address real business challenges.
Key Responsibilities

Research & Exploration

Continuously track the latest AI advancements, frameworks, and open-source projects.
Engage with research articles, open-source communities, and technical updates.


Rapid Prototyping & Development

Select, test, and implement AI tools and projects based on criteria set by the R&D Team Lead.
Build, integrate, and refine AI agents and workflows tailored to internal business needs.
Develop Minimum Viable Agents (MVAs) for projects prioritized by the innovation board.


Collaboration & ReportingWork closely with cross-functional teams to understand business problems and prototype solutions.
Document findings, experiments, and implementation results.
Report progress and outcomes directly to the R&D Team Lead.

Required Qualifications

Proven hands-on experience in AI/ML prototyping and workflow development.
Proficiency in Python and common AI/LLM frameworks (e.g., LangChain, LlamaIndex, Hugging Face, OpenAI API, etc.).
Strong understanding of agentic workflows and AI stack integration, and Model Context Protocol (MCP) for scalable AI system design.
Ability to rapidly evaluate, test, and implement open-source AI projects.
Solid grasp of software development practices (version control, testing, deployment).
Strong problem-solving skills and ability to translate business needs into technical solutions.


Preferred Qualifications

Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker).
Familiarity with APIs, microservices, and system integration.
Prior work in an R&D or innovation-focused environment.
Knowledge of data engineering and MLOps best practices.