Solutions Architect (AI)

TechConnect; Jakarta Utara; On-site; IDR 800,000,000 - 1,200,000,000; Full time

Job summary

TechConnect in Jakarta seeks an experienced Solution Architect (AI) to design and deliver end-to-end AI/ML and generative AI solutions that integrate with existing enterprise systems.

You will translate business use cases into scalable architectures, define data pipelines and MLOps, and mentor engineering teams while partnering with Data Science, Data Engineering, EA, and Product to move AI initiatives from proof-of-concept to production.

Qualifications

• 8–12+ years in solution or enterprise architecture roles
• 3+ years focused on AI/ML or generative AI
• Hands-on with LLMs, RAG architectures, prompt engineering, and vector databases
• Experience with major cloud AI platforms (Azure AI, AWS Bedrock/SageMaker, GCP Vertex AI)
• Strong data architecture, APIs, microservices, and enterprise integration
• Familiarity with responsible AI, data privacy regulations, and governance frameworks
• Bachelor's or Master's degree in CS/DS/Engineering or related field
• Cloud AI certification and EA certification

Responsibilities

• Design end-to-end AI/ML and generative AI solution architectures aligned to business requirements and enterprise standards
• Translate business use cases into technical designs including LLM integration and RAG pipelines
• Evaluate AI platforms, frameworks, and vendors and define data pipelines and MLOps practices
• Provide technical leadership and mentorship to engineering teams implementing AI solutions
• Document architectures, integration patterns, and key technical decisions for governance

Skills

8–12+ years in architecture

AI/ML focus 3+ years

Excellent communication

Education

Bachelor's or Master's in CS/DS/Engineering

Tools

Pinecone

Weaviate

pgvector

TensorFlow

PyTorch

Hugging Face

Azure AI/OpenAI Service

AWS Bedrock/SageMaker

GCP Vertex AI

Job description

Description

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.