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Open nowPosted 59 days ago

Solutions Architect (AI)

Workable (global search)108,016 open roles

Where
Jakarta, Indonesia
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Your applicationOpen nowSolutions Architect (AI)Workable (global search) · Jakarta, Indonesia

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7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 59 days ago

Workable (global search) median: 7 days open

The posting

The Solution Architect (AI) designs and delivers end-to-end AI, machine learning, and generative AI solutions that integrate cleanly into the enterprise's existing technology landscape. This role translates business use cases into scalable, secure, and governable solution architectures — evaluating platforms and vendors, defining data and MLOps pipelines, and partnering closely with Data Engineering, Data Science, Enterprise Architecture, and Product teams to move AI initiatives from proof-of-concept to production.

  • Design end-to-end AI/ML and generative AI solution architectures aligned to business requirements and enterprise architecture standards.
  • Translate business use cases into technical solution designs, including LLM integration, RAG (Retrieval-Augmented Generation) pipelines, and ML model deployment.
  • Evaluate and select AI/ML platforms, frameworks, and vendors (e.g., Azure AI/OpenAI Service, AWS Bedrock/SageMaker, GCP Vertex AI, open-source LLMs).
  • Define data pipelines and MLOps practices for model training, deployment, monitoring, versioning, and retraining.
  • Ensure AI solutions comply with data governance, security, and privacy requirements, and align with responsible AI principles.
  • Collaborate with Data Engineering, Data Science, Enterprise Architecture, and Product teams to embed AI capabilities into existing systems.
  • Build proofs-of-concept and prototypes to validate AI use cases before committing to full-scale implementation.
  • Provide technical leadership and mentorship to engineering teams implementing AI solutions.
  • Track emerging AI/GenAI technologies and advise leadership on adoption strategy and roadmap prioritization.
  • Document solution architectures, integration patterns, and key technical decisions for governance and knowledge continuity.

Requirements

  • 8–12+ years in solution or enterprise architecture roles, including 3+ years focused specifically on AI/ML or generative AI solutions.
  • Hands-on experience with LLMs, RAG architectures, prompt engineering, and vector databases (e.g., Pinecone, Weaviate, pgvector).
  • Practical experience with at least one major cloud AI platform: Azure AI/OpenAI Service, AWS Bedrock/SageMaker, or GCP Vertex AI.
  • Solid understanding of MLOps practices — model versioning, CI/CD for ML, monitoring, and automated retraining pipelines.
  • Proficiency in Python and familiarity with core ML frameworks (TensorFlow, PyTorch, Hugging Face).
  • Strong grounding in data architecture, APIs, microservices, and enterprise integration patterns.
  • Working knowledge of responsible AI principles, data privacy regulations (e.g., GDPR), and AI governance frameworks.
  • Excellent communication skills — able to translate complex AI concepts for both technical and non-technical stakeholders.
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Cloud AI certification (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, Google Cloud Professional ML Engineer).
  • Enterprise architecture certification (e.g., TOGAF), especially if the role will interface closely with the broader EA practice.
  • Experience standing up an AI Center of Excellence or AI governance framework from scratch.
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