Ai Expert/senior Architect

7 days ago

Singapore DUOTECH PTE. LTD. Full time

**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., **AliCloud, AWS, 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 **AliCloud, AWS, 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.

**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**: Alicloud AI PAI & EAS, AWS SageMaker, 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
- **Graph DBs**: ArangoDB, NebulaGraph
- **Time-series DBs**: Prometheus, OpenTSDB
- 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.

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