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Date:  Jul 28, 2026

AI Product Expert & Solutions Architect

Location: 

ID

Level: 
Employment Status:  Permanent
Department:  Group B2B ICT
Description: 

Minimum Requirements

Qualification:

  • Education: Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Information Systems, or a related quantitative technical field.
  • Certifications (Preferred but not mandatory): Professional certifications in Cloud Computing (AWS, Google Cloud, or Azure), Artificial Intelligence/Machine Learning, or Enterprise Architecture (e.g., TOGAF).

Related Experience:

  • Proven Delivery Track Record: Minimum requirements prioritize a demonstrated capability to ship products over a fixed years-of-experience threshold. Must have a proven track record of independently designing and shipping at least one custom AI solution into a live, production-integrated enterprise system.
  • Enterprise Integration: Hands-on experience designing and deploying application integration architectures within complex enterprise environments (cloud-native, hybrid, or on-premises).
  • Consultative & Client-Facing Role: Demonstrated experience in customer-facing, presales, or technical consulting capacity—specifically translating ambiguous business requirements into concrete technical solutions.
  • Production Troubleshooting: Experience acting as an advanced technical escalation point (L3 support or equivalent lead engineer role) resolving post-deployment architectural bottlenecks or production anomalies.

Skills:

Technical Skills

  • Generative & Agentic AI Architecture: Deep understanding of RAG (Retrieval-Augmented Generation) architectures (chunking strategies, vector databases, retrieval tuning) and hands-on experience with orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen, or similar).
  • Integration Patterns & Protocols: Mastery of REST API design/consumption, authentication patterns (OAuth, API keys), and async/event-driven integration (queues, webhooks). Familiarity with Model Context Protocol (MCP) or similar standards for tool-and-data integration is highly prioritized.
  • Core Engineering & Tools: Proficiency in Python and SQL. Practical experience with Git/version control, basic containerization (Docker), and cloud deployment practices.
  • Cost-Aware Systems Design: Ability to design AI solutions with practical engineering judgment, specifically modeling third-party API token costs, catching strategies, and optimizing infrastructure resources (e.g., computing/GPU requirements) to safeguard commercial margins.

 

Professional & Behavioral Competencies

  • Dealing with Ambiguity: Strong product instinct with the capability to drive project scope and move forward independently without fully specified briefs.
  • Consultative Communication: Exceptional ability to simplify highly complex AI technical concepts and present them persuasively to both technical teams (architects/delivery) and non-technical business executives.
  • Commercial & Business Acumen: Balanced mindset that aligns deep technical architecture decisions with execution speed, corporate business value, and operational viability.

Role Responsibilities

Role purposed

1. Product Strategy & MVP Development

- Translate customer feedback and market needs into concrete technical insights.

- Build rapid, early-stage Proof-of-Concepts (PoCs) for GenAI and Agentic workflows.

- Evaluate emerging AI frameworks, protocols (e.g., MCP), and models for product readiness.

2. Technical Consulting & Customer Engagement

- Lead deep-dive technical discovery and scope sessions with enterprise clients.

- Present and defend complex AI architectures to enterprise architects and security teams.

- Act as the trusted AI subject matter expert in high-value sales meetings.

3. Architecture Design & Technical Proposals

- Design end-to-end integration and application architectures for production-grade AI solutions.

- Author comprehensive technical sections for proposals, RFPs, and Scope of Work (SoW) documents.

- Conduct cost-aware architecture analysis, estimating token usage and infrastructure needs.

4. Level 3 (L3) Delivery Support

- Act as the final technical escalation point for post-deployment production anomalies.

- Troubleshoot complex AI issues such as model drift, prompt injection, and agent routing failures.

- Conduct post-mortem reviews on architectural failures to improve system resilience.

5. Capability Enablement & Knowledge Management

 

- Create and maintain reusable technical assets, reference architecture, and demo kits.

- Conduct training and enablement sessions for Sales, Presales, and Delivery teams.

- Mentor technical delivery teams on advanced AI engineering tools

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