Lead AI Engineer / AI Solutions Architect (Remote)

Application Process

https://forms.gle/4Hdj8CqbWjufQxAdA

About Us

Hipster Inc. is a Singapore-headquartered technology company building enterprise software, AI solutions, cloud platforms and digital products for businesses and government organisations.

We also developed Voncierge, our own AI-powered customer engagement platform, deployed at Changi Airport and Jewel Changi. Voncierge connects customers with AI agents or live representatives through web, mobile, WhatsApp, kiosks and other digital channels.

We are expanding Voncierge into an enterprise AI agent platform capable of understanding customer requests, retrieving information, executing business workflows and integrating with enterprise systems.

Alongside Voncierge, Hipster delivers custom AI and enterprise technology solutions through commercial projects, government tenders and open innovation programmes.

The Role

We are looking for a hands-on Lead AI Engineer / AI Solutions Architect who can design, build, deploy and scale enterprise-grade AI systems.

You will take technical ownership of AI architecture across both Voncierge and Hipster, working on everything from conversational AI agents and enterprise workflow automation to secure cloud infrastructure and open-source model customisation.

This is not a research-only or prompt-engineering role. You must be capable of independently designing system architecture, writing production code, deploying solutions and solving complex technical challenges.

You will work closely with our CEO, product managers, designers and engineering teams to turn AI concepts into reliable, commercially viable products.

Key Responsibilities

1. Enterprise AI Architecture & Engineering

• Design and build secure, scalable, multi-tenant AI systems for enterprise and government clients.
• Own end-to-end architecture, from technical design and model selection to deployment and optimisation.
• Develop production-grade AI services, backend APIs and integrations.
• Build reusable AI components and frameworks for deployment across multiple products and customers.
• Make architectural decisions considering performance, scalability, security, latency and cost.

2. AI Agents & Workflow Automation

• Build intelligent AI agents capable of reasoning, tool calling, API execution and multi-step workflow automation.
• Implement agent orchestration using LangGraph, LangChain, LlamaIndex or similar frameworks.
• Design agent memory, context management, state persistence, human approvals and escalation workflows.
• Integrate agents with CRM, ERP, booking systems, payment platforms, ticketing systems and enterprise APIs.
• Build multilingual AI solutions for web, mobile, WhatsApp, voice and kiosk environments.
• Develop AI agents that perform real business transactions rather than simply answer questions.

3. LLMs & Open-Source Model Customisation

• Evaluate and integrate commercial AI models, including OpenAI, Anthropic, Gemini and AWS Bedrock.
• Deploy, configure and customise open-source models such as Llama, Qwen and Mistral.
• Apply fine-tuning techniques such as LoRA, QLoRA and PEFT where appropriate.
• Work with Hugging Face, vLLM and other model deployment and inference frameworks.
• Optimise model performance, accuracy, latency, GPU utilisation and inference costs.
• Evaluate self-hosted, cloud-hosted and private AI deployments based on customer requirements.
• Determine when RAG, fine-tuning, prompting or conventional software engineering provides the best solution.

4. RAG, Knowledge Systems & Document Intelligence

• Architect enterprise RAG systems using structured and unstructured data.
• Implement embeddings, vector databases, hybrid search, reranking and knowledge retrieval.
• Work with technologies such as pgvector, Qdrant, OpenSearch, Pinecone or similar platforms.
• Build secure, tenant-isolated knowledge bases with appropriate access controls.
• Develop document processing solutions involving OCR, PDF extraction, semantic search and AI-powered analysis.
• Support AI-driven tender analysis, requirement extraction, proposal generation and enterprise document automation.

5. Cloud Architecture & Enterprise Security

• Design and deploy AI infrastructure primarily on AWS, with knowledge of Azure or other cloud platforms.
• Work with services such as AWS Bedrock, SageMaker, Lambda, ECS/EKS, RDS, S3, IAM and VPC.
• Implement Docker, Kubernetes, CI/CD pipelines and infrastructure as code.
• Design secure cloud networking, identity management, encryption, access controls, logging and monitoring.
• Apply AI security practices to prevent prompt injection, data leakage and unauthorised tool execution.
• Ensure appropriate data isolation, residency, retention and privacy controls.
• Support technical security requirements aligned with ISO 27001, SOC 2, Singapore PDPA and enterprise security standards.
• Design reliable systems with appropriate backup, disaster recovery and availability controls.

6. AI Performance, Quality & Cost Optimisation

• Develop automated evaluation frameworks to measure AI accuracy, reliability and task completion.
• Implement observability, tracing, monitoring and testing for AI agents.
• Optimise token consumption, model routing, inference latency and cloud infrastructure costs.
• Build fallback mechanisms, error recovery and human-in-the-loop safeguards.
• Evaluate technical trade-offs between commercial APIs and self-hosted open-source models.
• Ensure AI applications remain cost-effective as customer usage scales.

7. Technical Leadership & Enterprise Delivery

• Translate business requirements, government tenders and innovation challenges into practical AI solutions.
• Support solution architecture, technical feasibility studies, POCs, estimates and technical proposals.
• Collaborate with project managers, designers, backend engineers, frontend engineers and QA teams.
• Review code, guide technical implementation and mentor engineers.
• Present technical architectures and explain engineering decisions to internal and external stakeholders.
• Take ownership of technical delivery from initial prototype to production.

What You Will Work On

Over the next 12 months, our AI roadmap includes:

• Voncierge AI Agents: Intelligent multilingual agents for customer enquiries, booking, transactions, airport services and human escalation.
• Enterprise AI Agent Platform: Reusable agent orchestration, knowledge retrieval, API integrations, business rules and tenant-level configurations.
• Voice & Conversational AI: Real-time multilingual voice interactions, speech recognition, text-to-speech and integration with live customer service.
• AI Document Intelligence: Automated document processing, knowledge extraction, tender analysis and proposal assistance.
• Custom Enterprise AI Systems: AI solutions for government tenders, IMDA Open Innovation Platform challenges and commercial clients.
• Private AI Deployments: Customised open-source models for customers requiring greater control over data privacy, infrastructure and operating costs.

Requirements

• 7+ years of professional software engineering experience, with strong backend and system architecture skills.
• At least 2 years of hands-on experience developing and deploying production AI/LLM applications.
• Strong proficiency in Python, including frameworks such as FastAPI.
• Hands-on experience with LangGraph, LangChain or equivalent agent orchestration frameworks.
• Practical experience building RAG systems, vector databases and enterprise API integrations.
• Experience deploying and customising open-source LLMs, with understanding of fine-tuning and inference optimisation.
• Strong understanding of AWS or Azure, cloud architecture, security, Docker and CI/CD.
• Experience designing scalable, reliable and secure backend systems.
• Understanding of AI evaluation, monitoring, cost optimisation and production troubleshooting.
• Ability to independently own architecture and implement critical technical components.
• Strong English communication skills and ability to collaborate with remote engineering teams.

We prioritise demonstrable technical expertise and real production experience over certifications or academic qualifications.

Good to Have

• Experience with multilingual voice AI, streaming audio, speech-to-text and text-to-speech systems.
• Experience with computer vision, OCR or multimodal AI.
• Experience with GPU infrastructure, model quantisation, fine-tuning or distillation.
• Familiarity with WebRTC, real-time video systems or AI avatars.
• Experience delivering AI solutions for government, airports, healthcare or security-sensitive enterprises.
• Relevant AWS, Azure, AI or cloud security certifications.
• Experience mentoring engineers or leading technical architecture decisions.

Why Join Us?

• Build AI solutions used in real enterprise environments, not just experimental prototypes.
• Work on an existing AI platform with opportunities to shape its next stage of growth.
• Take ownership of technical architecture and key engineering decisions.
• Work across agentic AI, LLMs, open-source models, cloud infrastructure and enterprise integrations.
• Collaborate directly with leadership and a cross-functional product and engineering team.
• Work remotely with a Singapore-headquartered technology company.
• Grow into broader technical leadership as our AI products and enterprise projects scale.

Application Process

Interested candidates should submit their CV, LinkedIn or GitHub profile, and details of relevant production AI projects.

Shortlisted candidates will be invited to complete a technical design assessment, followed by an interview.