AI Tech Architect

About Hytech
Hytech is a leading management consulting firm headquartered in Australia and Singapore, specialising in digital transformation for fintech and financial services companies. We provide comprehensive consulting solutions, as well as middle- and back-office support, to empower our clients with streamlined operations and cutting-edge strategies.

With a global team of over 2,000 professionals, Hytech has established a strong presence worldwide, with offices in Australia, Singapore, Malaysia, Taiwan, Philippines, Thailand, Morocco, Cyprus, Dubai and more.

Introduction
We are looking for experienced and forward-thinking AI Architects to join the design and implementation of scalable, cloud-native AI infrastructure. In this role, you will be responsible for setting up robust MLOps pipelines, building multi-modal feature platforms (including vector, graph, and sequence data stores), and delivering production-grade model training and deployment workflows.
You’ll work closely with engineering, product, and data teams to leverage cloud infrastructure (e.g., , AWS (Main), AliCloud, GCP, Azure, ) to deliver scalable, cost-efficient, and secure AI systems.

Key Responsibilities
Architect and build end-to-end AI infrastructure across model lifecycle stages — from feature engineering to model training, deployment, and monitoring — using cloud-native technologies.
Design and maintain feature pipelines for:
Vector databases (e.g., FAISS, Weaviate, Milvus) for semantic embedding retrieval
Graph databases (e.g., Neo4j, TigerGraph) for network-based inference and entity linking
Sequence/time-series databases (e.g., InfluxDB, TimescaleDB) for temporal pattern modeling and real-time monitoring
Lead the design of a centralized feature store platform to support consistent, reusable ML features across teams.
Develop MLOps workflows using cloud orchestration tools and infrastructure-as-code to automate training, validation, deployment, and monitoring.
Leverage AWS, AliCloud, GCP, and Azure services (e.g., SageMaker, Vertex AI, EAS, GKE, ECS) to optimize infrastructure for scalability, availability, and cost.
Integrate model serving platforms (e.g., Triton, Ray Serve, BentoML, vLLM) for low-latency inference at scale.
Establish observability for ML pipelines: model drift, feature staleness, and data quality monitoring.
Collaborate with AI Scientist and application teams to productionize new models and LLM-based systems.

Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or Systems Engineering.
6+ years of experience in building AI/ML platforms, cloud-native architecture, or infrastructure engineering.
Hands-on experience with:
MLOps frameworks: MLflow, Kubeflow, Metaflow, Airflow
Feature stores: Feast, Tecton, custom in-house platforms
Cloud services: AWS SageMaker, AliCloud AI PAI & EAS, GCP Vertex AI, Azure ML
Container orchestration and deployment using Docker and Kubernetes
Deep understanding of distributed systems, CI/CD for ML, and scalable data & model pipelines.

Preferred Qualifications
Practical experience with multi-database architecture:
Vector DBs: Qdrant, Pinecone, Vespa, Opensearch
Graph DBs: ArangoDB, NebulaGraph, Neo4j, MemGraph
Time-series DBs: Prometheus, OpenTSDB, Lindorm
Experience supporting LLM and embedding model deployments (e.g., vLLM, DeepSpeed, HuggingFace inference endpoints)
Familiarity with GPU scheduling, cost optimization, and hybrid/multi-cloud architecture patterns.
Contributions to internal platforms or developer tools enabling teams to deploy models autonomously.
Proven ability to drive infra decisions across cross-functional teams in a fast-paced environment.

What We Offer
Competitive compensation and equity package.
Ownership to design foundational AI infrastructure across cloud and hybrid environments.
Opportunity to shape the next generation of scalable AI systems from infrastructure to application layer.
Collaboration with world-class engineers, researchers, and domain experts.