AI Solution Architect

St Engineering; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

ST Engineering seeks an AI Solution Architect to lead design, evaluation, and rollout of Agentic AI applications across the enterprise. You will identify opportunities, conceptualize solutions, and drive implementation by building in-house or integrating external platforms.

The role requires deep knowledge of LLMs, RAG, prompt engineering, and MLOps, with strong solution-selling and leadership skills to partner with IT, Data Science, and business units.

Qualifications

• Proven experience architecting enterprise-scale AI solutions.

Responsibilities

• Identify and evaluate opportunities to implement Agentic AI solutions aligned with business strategy.

Skills

Enterprise AI architecture

LLMs & multimodal models

Prompt engineering

MLOps & deployment

English communication

Education

Bachelor's degree in CS/SE or related

Tools

LangChain

CrewAI

AutoGen

Job description

We are seeking a forward-thinking AI Solution Architect to lead the design, evaluation, and rollout of cutting-edge Agentic AI applications across the enterprise. The role requires both technical depth and business acumen, with the ability to identify opportunities, conceptualize solutions, and drive implementation—whether by building in-house or integrating with best-in-class external platforms.

The ideal candidate will possess a deep understanding of the Agentic AI ecosystem, be comfortable navigating the rapidly evolving world of large language models (LLMs), and demonstrate strong technical, solution-selling, and leadership skills

Key Responsibilities

• Identify and evaluate opportunities to implement Agentic AI solutions aligned with business strategy.
• Conceptualize and architect AI applications leveraging both in-house development and vendor solutions ("make or buy" approach).
• Ensure scalable, secure, and compliant AI solution designs that integrate seamlessly into enterprise IT and data platforms.
• Agentic AI Ecosystem Ownership
• Define and maintain the blueprint for an Agentic AI ecosystem—including LLMs, Retrieval-Augmented Generation (RAG), prompt engineering frameworks, MLOps, orchestration tools, and monitoring.
• Partner with data engineering, and cybersecurity teams to ensure robust infrastructure and governance.
• Implementation & Rollout
• Build, test, and deploy Agentic AI applications for diverse use cases across ST Engineering business units.
• Manage solution pilots, proof-of-concepts, and enterprise-wide rollout projects.
• Establish the best practices and reusable frameworks for Agentic AI adoption.
• Collaborate with business leaders to translate business needs into AI solutions.
• Present AI solution proposals to senior stakeholders in clear, impactful ways, highlighting value, ROI, and risk considerations.
• Drive adoption and organizational buy-in through effective communication and change management.
• Work closely with cross-functional teams including IT, Data Science, and business units.
• Mentor and guide team members on Agentic AI techniques, frameworks, and best practices.
• Champion a collaborative, innovative, and learning-oriented culture.

Job Requirements:

Must-Have:

• Proven experience architecting and implementing enterprise-scale Agentic AI solutions.
• Strong expertise in:
  • Large Language Models (LLMs) and multimodal models.
  • Prompt engineering strategies and frameworks.
  • Retrieval-Augmented Generation (RAG).
  • MLOps, model deployment, and lifecycle management.
• Strong expertise with agentic AI frameworks (LangChain, CrewAI, AutoGen, etc.).
• Excellent programming skills (e.g., Python, Java, or equivalent).
• Strong communication and solution-selling skills—capable of engaging both technical and business stakeholders.
• Demonstrated ability to lead and work within multi-disciplinary teams.
• Proficiency in English, both written and spoken.
• Strong problem-solving skills with a balance of strategic vision and execution focus.
• Hands-on exposure to cloud-based AI/ML platforms (AWS, Azure, GCP).
• Prior experience in enterprise IT transformation or digital innovation programs.

Plus:

• Knowledge of enterprise data platforms, APIs, and systems integration.
• Experience in Data Science and Machine Learning model development.
• Industry certifications in AI/ML, cloud, or enterprise architecture.