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.
