Senior ML /AI

EXCELTECH COMPUTERS PTE. LTD.; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

EXCELTECH COMPUTERS PTE. LTD. seeks a Senior ML/AI Engineer to productionise and operate ML/AI workloads on the SWDA data platform using AWS and Databricks. Location at Paya Lebar in Singapore.

Ideal candidates have 4–7 years delivering end-to-end ML systems, with strong Python, Spark/PySpark, SQL, and experience building lakehouse pipelines. Hands-on with Databricks, MLflow, and AWS services is essential for this role.

Qualifications

• Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, code review, debugging and modular design.
• Hands‑on experience with Databricks and MLflow for experiment tracking, model registry, workflow orchestration or model serving.
• Strong hands‑on experience with Spark/PySpark and SQL, including distributed data processing, query optimisation, partitioning, joins, incremental processing and troubleshooting data pipelines.
• Experience designing and implementing lakehouse or data‑lake pipelines using Databricks, Delta Lake and AWS S3, including Bronze, Silver and Gold data‑layer patterns.
• Experience with data modelling, schema design, data contracts, metadata, lineage, data‑quality validation and reconciliation for production analytics and ML datasets.
• Experience orchestrating batch or streaming workflows using Databricks Workflows, AWS Glue, Step Functions, EventBridge, Apache Airflow or an equivalent enterprise orchestration tool.
• Hands‑on AWS data and cloud engineering experience with a relevant mix of S3, Glue, Lake Formation, IAM, KMS, ECR, ECS/EKS, Lambda, Step Functions, EventBridge, CloudWatch, API Gateway, Secrets Manager, Redshift, MSK/Kinesis or equivalent approved services.
• Experience with Docker, infrastructure as code such as Terraform, and CI/CD platforms such as GitLab CI, GitHub Actions, Jenkins or Harness.

Skills

Python

APIs

packaging

automated testing

Git

code review

debugging

modular design

Spark/PySpark

SQL

data modelling

schema design

data contracts

metadata

data-quality validation

reconciliation

batch/streaming orchestration

AWS data & cloud engineering

Tools

Databricks

MLflow

Delta Lake

AWS S3

Glue

Lake Formation

IAM

KMS

ECR

ECS/EKS

Lambda

Step Functions

EventBridge

Apache Airflow

CloudWatch

API Gateway

Secrets Manager

Redshift

MSK/Kinesis

Docker

Terraform

CI/CD (GitLab CI / GitHub Actions / Jenkins / Harness)

Job description

Senior ML /AI

Work location- Paya Lebar

The ML/AI Engineer sits within the DSAD Platform and Architecture Team and is responsible for productionising, operating and improving machine-learning and AI workloads on the SWDA data platform using AWS and Databricks.

Experience: Typically 4-7 years of relevant software, data or ML engineering experience, including hands‑on delivery and operation of ML/AI systems beyond prototype stage. Candidates with fewer years but strong evidence of end‑to‑end production ownership may also be considered.

B. Technical & Professional Competencies

Essential (Must-Have)

• Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, code review, debugging and modular design.
• Hands‑on experience with Databricks and MLflow for experiment tracking, model registry, workflow orchestration or model serving.
• Strong hands‑on experience with Spark/PySpark and SQL, including distributed data processing, query optimisation, partitioning, joins, incremental processing and troubleshooting data pipelines.
• Experience designing and implementing lakehouse or data‑lake pipelines using Databricks, Delta Lake and AWS S3, including Bronze, Silver and Gold data‑layer patterns.
• Experience with data modelling, schema design, data contracts, metadata, lineage, data‑quality validation and reconciliation for production analytics and ML datasets.
• Experience orchestrating batch or streaming workflows using Databricks Workflows, AWS Glue, Step Functions, EventBridge, Apache Airflow or an equivalent enterprise orchestration tool.
• Hands‑on AWS data and cloud engineering experience with a relevant mix of S3, Glue, Lake Formation, IAM, KMS, ECR, ECS/EKS, Lambda, Step Functions, EventBridge, CloudWatch, API Gateway, Secrets Manager, Redshift, MSK/Kinesis or equivalent approved services.
• Experience with Docker, infrastructure as code such as Terraform, and CI/CD platforms such as GitLab CI, GitHub Actions, Jenkins or Harness.