Solutions Architect (AI)

Techconnect.id; Jakarta Pusat; On-site; IDR 350,000,000 - 700,000,000; Full time

Job summary

Techconnect.id is seeking a Solution Architect (AI) to design and deliver end-to-end AI/ML solutions that fit into our enterprise technology landscape. You will translate business use cases into scalable architectures and collaborate with Data Engineering, Data Science, EA, and Product teams to move AI initiatives from proof-of-concept to production.

The role requires hands-on experience with LLMs, RAG pipelines, and cloud AI platforms, plus strong MLOps, data governance, and security practices.

Qualifications

• 8-12+ years in solution or enterprise architecture roles, including 3+ years focused on AI/ML or generative AI.
• Hands-on experience with LLMs, RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate, pgvector).
• Practical experience with at least one major cloud AI platform: Azure AI/OpenAI Service, AWS Bedrock/SageMaker, or GCP Vertex AI.
• Proficiency in Python and core ML frameworks (TensorFlow, PyTorch, Hugging Face).
• Strong grounding in data architecture, APIs, microservices, and enterprise integration patterns.
• Excellent communication skills.
• Cloud AI certification (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning - Specialty, Google Cloud Professional ML Engineer).
• TOGAF and AI governance framework experience.

Responsibilities

• 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).
• 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.

Skills

AI architecture

LLM integration

RAG pipelines

MLOps

Python

Data governance

Enterprise integration

Communication

Cloud certifications

Center of Excellence

Prompt engineering

Vector databases

Education

Bachelor’s or Master’s degree in CS/DS/Engineering

Tools

Azure AI/OpenAI Service

AWS Bedrock/SageMaker

GCP Vertex AI

Pinecone

Weaviate

pgvector

Hugging Face

Job 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.

• 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 - ...

• 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.