The (Senior) Solution Analyst (AI/Intelligent Automation) in the Central Data & Business Analytics (CDBA) team will help shape how the company uses AI and automation to run smarter and scale better. Reporting to the AI Domain Leader, you will design and deliver Python/SQL and LLM-based applications on Azure, drive DevSecOps and engineering best practices, build end-to-end intelligent automation, and guide junior developers while collaborating with other technical teams. This is a hands-on, high-impact role for someone who enjoys building robust AI solutions end-to-end and wants to grow as a technical leader in AI engineering and intelligent automation within a global organization.
Principal Responsibilities:
Solution Design & Architecture
Translate business and analytics requirements into end-to-end application architectures using Python, SQL, and Azure services (e.g., Functions, App Service, Data Factory, Storage, SQL).
Design modular, object-oriented components, microservices, and APIs with reusable libraries and clear configuration management.
Define data models and optimize database structures and queries.
Guide junior developers to ensure designs are understood, correctly implemented, and aligned with standards.
AI / LLM Application Development
Design and implement AI and LLM-based solutions for intelligent automation, document understanding, and decision support.
Build and maintain prompt workflows, evaluation logic, integrations with internal systems/APIs/data sources.
Implement validation and quality checks to improve robustness and reduce hallucination.
Intelligent Automation (E2E)
Design and implement end-to-end automation solutions from data ingestion and processing to reporting and user delivery.
Build pipelines to process structured and unstructured data (e.g., documents, PDFs, emails, logs) using Python, SQL, and AI/ML techniques where appropriate.
DevSecOps & Engineering Best Practices
Establish and maintain CI/CD pipelines for AI/Python projects, including automated testing and code quality checks.
Maintain project templates, scaffolding, and coding standards across AI/automation projects, partnering with relevant teams to align with corporate security and compliance standards.
Reliability, Performance & Cost Management
Monitor and optimize application performance and scalability
Track and optimize AI/LLM usage, cost, and capacity.
Design and maintain logging, monitoring, dashboards, and alerting for production workloads.
Delivery & Agile Ways of Working
Work within Agile or hybrid methodologies to plan, estimate, and deliver features iteratively with clear acceptance criteria.
Coordinate with business stakeholders, analysts, and technology teams to clarify requirements, manage dependencies, and support UAT, production cutover, and stabilization.
Technical Mentoring & Team Collaboration
Provide technical guidance and code reviews for junior developers and analysts on Python, SQL, OOP, cloud engineering, and AI/LLM development.
Promote a disciplined engineering culture focused on structured, scalable, and repeatable frameworks, sharing best practices and helping institutionalize them within the team.
Job Level Specifications:
Essential
Strong analytical and problem-solving skills; able to design pragmatic yet scalable technical solutions under time constraints.
Proven ability to work as part of a team, collaborating with junior developers, peers, and other technical teams.
Ability to influence technical decisions, provide structured guidance, and support the AI Domain Leader in driving AI/automation technical direction.
Excellent verbal and written communication skills in both English and Mandarin; able to explain complex technical topics and insights to technical and non-technical stakeholders.
Able to translate ambiguous business needs into clear technical requirements and AI/automation designs.
High level of ownership, quality orientation, and attention to detail in delivering production-grade AI and automation solutions.
Desirable
Awareness of responsible AI principles (data privacy, fairness/bias, safe use of LLMs) and ability to factor these into solution design.
Familiarity with formal change management and governance processes (e.g., change approvals, release management).
Track record of continuous improvement in engineering and analytics practices (e.g., introducing new templates, frameworks, or analytical approaches).
Work Experience:
Essential
Typically 5+ years of hands-on software engineering / application development, with strong focus on:
Python, SQL, and OOP application development (must have).
Designing and building microservices or modular backend services in production.
LLM development, prompt engineering, and systematic evaluation (must have), including at least one real-use project.
Proven experience delivering cloud-based solutions on Microsoft Azure (or similar), integrating with databases, storage, or messaging services.
Hands-on experience with CI/CD, automated testing, and Git-based source control in a team environment.
Exposure to DevSecOps practices.
Experience in data-centric work, such as building data pipelines, data cleaning/transformation, or supporting analytics use cases.
Desirable
Experience with database performance tuning and optimization.
Experience with containerization and orchestration (e.g., Docker, Kubernetes).
Experience with agentic LLM patterns, RAG, and LLM application frameworks or orchestration tools (e.g., LangChain, Semantic Kernel, or similar).
Experience with data analytics or ML to support business analysis.
Prior involvement in internal frameworks, reusable libraries, or template repositories for Python/AI or automation projects.
Familiarity with AI-assisted coding tools (e.g., GitHub Copilot or similar).
Education and Certification(s):
Essential
Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related field; or equivalent practical experience.
Desirable
Master’s degree in a related discipline.
Professional certifications in Microsoft Azure, software engineering, or security/DevSecOps.
Distinguishing Characteristics:
Key technical partner to the AI Domain Leader, turning AI/automation strategy into practical technical roadmaps and solutions.
Hands-on “player-coach” who builds production-quality AI/automation solutions and mentors junior developers.
Strong product and engineering mindset with integrity, self-learning, proactive ownership, and clear communication.
Drives standardized, secure, well-governed AI/Python DevSecOps and intelligent automation practices.
The above statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills.
