Lead AI Solution Architect

About the role

Key Responsibilities

• Own end‑to‑end architecture for AI, GenAI, data, integration, and cloud‑native solutions in Google Engineer business engagements.
• Translate business requirements and customer priorities into reference architectures, solution blueprints, estimates, delivery roadmaps, and technical decisions.
• Design production‑ready LLM, RAG, agent, multimodal, document intelligence, and workflow automation solutions.
• Apply Google Cloud and Vertex AI services such as Gemini, BigQuery, Cloud Storage, Cloud Run, GKE, Pub/Sub, Dataflow, Vertex AI Pipelines, Model Registry, IAM, and observability services where appropriate.
• Define non‑functional requirements for security, privacy, reliability, scalability, latency, maintainability, and cost efficiency.
• Lead architecture reviews, design reviews, technical proposals, code‑quality governance, and production‑readiness assessments.
• Guide the transition from research or PoC to production, including evaluation, guardrails, CI/CD, monitoring, incident readiness, and operational ownership.
• Mentor AI, cloud, and software engineers; facilitate technical decisions; and remove engineering blockers across distributed teams.
• Communicate technical trade‑offs clearly to customers, delivery leaders, and non‑technical stakeholders.
• Identify delivery risks, dependencies, and opportunities for reusable platforms, accelerators, and engineering standards.

Essential Qualifications

• Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• At least 8 years of professional experience in solution architecture, software engineering, AI/ML engineering, cloud architecture, or a closely related field, including significant technical leadership experience.
• Proven hands‑on delivery of enterprise AI or Generative AI solutions from architecture and PoC through production operation.
• Strong Google Cloud experience, including practical use of Vertex AI and related services; working knowledge of Gemini, BigQuery, Cloud Run or GKE, IAM, networking, data services, and observability.
• Deep understanding of LLM application patterns, RAG, embeddings, vector search, agent orchestration, prompt engineering, model evaluation, safety controls, and AI governance.
• Strong software engineering fundamentals in Python and API or microservice design, with the ability to review implementation quality and operational readiness.
• Experience leading customer‑facing technical discussions, architecture workshops, solution proposals, and delivery decisions in an international environment.
• Professional English communication skills and the ability to collaborate effectively with distributed engineering and stakeholder groups.

Preferred Qualifications

• Google Cloud Professional Cloud Architect, Professional Machine Learning Engineer, or equivalent certification.
• Experience with Google Agent Development Kit, TensorFlow, JAX, LangChain, LangGraph, or comparable frameworks.
• Experience with responsible AI, security architecture, data governance, FinOps, and regulated enterprise environments.
• English language proficiency or experience working with APAC‑based stakeholders.

Success Measures

• Production solutions meet agreed reliability, security, quality, latency, and cost targets.
• Architecture decisions are documented, reusable, and aligned with Google Cloud engineering standards.
• PoCs are converted into viable production services with clear operational ownership.
• Customer and delivery stakeholders receive timely, evidence‑based technical guidance.
• Engineering capability improves through mentoring, reusable assets, and consistent review practices.

Benefits and Working Conditions

• Competitive compensation package, with 100% salary during probation, subject to company policy and the employment offer.
• Full social insurance contribution based on the applicable salary basis.
• Annual performance bonus and salary review in line with company policy.
• Premium healthcare insurance for employees and eligible family members from probation, subject to policy.
• Annual health check‑up and access to company‑sponsored training, professional courses, and certification examinations.
• Paid leave and public holidays in accordance with Vietnamese regulations and NTT DATA Vietnam policy.
• Hybrid working model, flexible start time with manager approval, and a global environment with international collaboration.
• Clear career development opportunities in cloud engineering, AI delivery, solution architecture, and technical leadership.

Work Schedule

• Monday to Friday, 08:00–12:00 and 13:00–17:00.
• Flexible start time between 07:45 and 09:00 with manager approval.