AI & Data Solutions Architect (Pre-sales)

Job Title: AI & Data Solutions Architect (Pre-sales)

Level: Mid to Senior

Location: Hong Kong (On-site)

Job Description:

MEGAZONE CLOUD is seeking a commercially-driven, customer-obsessed AI & Data Pre-Sales Solutions Architect to be the technical engine of our sales team. We know that successful AI initiatives are built on rock-solid data foundations. Therefore, we need an expert who brings a strong data engineering background to architect solutions that turn raw data into advanced cloud and Generative AI realities.

Partnering directly with the business development team, you will translate complex customer challenges into tangible, revenue-generating opportunities. If you excel at building trust, designing intelligent data-to-AI pipelines, and adapting quickly to the bleeding edge of AI technology, this is the perfect role to leverage your skills at the intersection of technology, data, and sales.

What You Will Be Doing:

* Data-First AI Sales Leadership: Drive the technical sales cycle from discovery to deal closure. Lead discovery sessions to uncover pain points, explicitly assessing a client’s data readiness and positioning Megazone Cloud’s data modernization and AI/ML offerings as the solution.

* Architecture & Commercial Design: Architect compelling, commercially viable solutions that bridge the gap between data engineering and AI. Design robust data lakes/warehouses, scalable ETL/ELT pipelines, and modern MLOps/LLM workflows. You will create technical proposals, respond to RFPs/RFIs, and develop detailed SOWs.

* Value-Driven Demonstrations: Develop impactful Proofs-of-Concept (POCs) that showcase clear business value. Prove to clients how cleaning and structuring their data unlocks powerful Generative AI use cases and automated workflows, helping them make confident investments.

* Strategic Customer Influence: Act as a trusted advisor to everyone from data engineers to C-level executives. Guide their technology roadmaps, helping them navigate the rapidly shifting AI landscape with future-proof, scalable data architectures.

* Continuous Tech Evangelism: Stay ahead of the hyper-paced AI and Data curve. Act as the subject matter expert on modern data platforms, Agentic AI, and LLM implementations, clearly articulating the ROI and competitive advantages of our solutions.

* Sales Enablement: Conduct technical workshops for customers and upskill internal sales teams on how to identify "data modernization for AI" opportunities.

Required Skills & Qualifications:

* Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

* Pre-Sales/Consulting Experience: 3+ years in a dedicated pre-sales, technical consulting, or solutions architecture role, with a proven track record of securing technical wins for complex, enterprise-grade projects.

* Essential Data Engineering Foundation: Hands-on background in data engineering. You must deeply understand data modeling, data integration (ETL/ELT), and data governance. Proficiency in Python and SQL is required.

* Deep AI/Data Domain Knowledge: Demonstrable experience in two or more of the following areas:

* Modern Data Platforms: Snowflake, BigQuery, Redshift, Databricks, or data streaming (Kafka, Kinesis).

* AI/ML & MLOps: Machine learning frameworks, SageMaker/Vertex AI/Azure ML, and operationalizing models from data pipeline to production.

* Generative AI: Practical implementation of LLMs, RAG architectures, and vector databases.

* Commercial Acumen: Strong business skills with the ability to build and present compelling business cases, translating complex data/AI architecture into executive-level ROI.

* Exceptional Communication: Outstanding presentation and whiteboarding skills. Ability to simplify complex technical concepts and navigate ambiguity in a fast-paced environment.

* Language: Business fluency in both Cantonese and English is essential.

Ways to Stand Out From The Crowd:

* Cloud Data Expertise: Deep expertise with at least one major cloud provider (AWS, Azure, or GCP), specifically using their core Data and AI services.

* Agentic AI & Automation: Familiarity with low-code or enterprise agentic AI frameworks (e.g., Dify, n8n, Copilot Enterprise, Gemini Enterprise, or LangChain/LlamaIndex).

* Certifications: Professional certifications from a major cloud provider (e.g., AWS Certified Machine Learning / Data Analytics, Google Professional Machine Learning Engineer / Data Engineer).

* DevOps/IaC: Hands-on experience with Infrastructure-as-Code tools (Terraform, CDK, CloudFormation) and containerization (Docker, Kubernetes).

* Industry Experience: Experience architecting solutions for highly regulated industries, particularly Financial Services (FSI).