MLOps Engineer (Databricks & AWS) - Gov - Contract

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

NEXBRIDGE RECRUITMENT PTE. LTD. is seeking an experienced MLOps/AIOps engineer to design and implement platform components for experiment tracking, deployment, monitoring and retraining using Databricks, MLflow and AWS.

You will productionise ML/AI prototypes into scalable pipelines and services, optimise Spark workloads and establish governance, security and cost controls while collaborating across teams.

Qualifications

• Typically 4-7 years of relevant software, data or ML engineering experience, with hands-on experience deploying and operating production ML/AI systems.
• Strong Python programming and software engineering skills, including API development, automated testing, Git and modular design.
• Hands-on experience with Databricks and MLflow for experiment tracking, model registry, workflow orchestration or model serving.
• Strong proficiency in PySpark, Apache Spark and SQL, including distributed data processing and pipeline optimisation.
• Experience with Databricks, Delta Lake and AWS S3 for production data pipelines and lakehouse architectures.
• Experience with feature engineering, data validation, model evaluation, deployment, monitoring and retraining.
• Familiarity with AWS services such as Glue, IAM, Lambda, Step Functions, CloudWatch and related cloud services.
• Experience with Docker, CI/CD tools and infrastructure-as-code solutions such as Terraform.
• Knowledge of model monitoring, drift detection, reproducibility, rollback procedures and cloud security.
• Strong problem-solving, communication and cross-functional collaboration skills.

Responsibilities

• Design and implement MLOps/AIOps platform components for experiment tracking, model deployment, monitoring and retraining using Databricks, MLflow and AWS.
• Productionise ML/AI prototypes into scalable, tested and maintainable pipelines, APIs and model-serving services.
• Build and maintain feature engineering pipelines using Databricks, PySpark, SQL and AWS.
• Develop automated CI/CD/CT workflows for code, data, models and infrastructure deployment.
• Implement model versioning, experiment tracking, data lineage, reproducibility and governance controls.
• Monitor system health, model performance, data quality, drift, resource utilisation and cloud costs.
• Optimise Spark workloads, data pipelines and model-serving infrastructure for performance and reliability.
• Establish model evaluation, regression testing, A/B testing and deployment rollback processes.
• Implement cloud security, access controls, audit logging and Responsible AI safeguards.
• Support recommendation and search systems through model maintenance, retraining, experimentation and performance optimisation.
• Maintain technical documentation, operational runbooks and deployment records, and support knowledge transfer across teams.

Skills

MLOps

AIOps

Python

CI/CD

APIs

Git

Communication

Cross-functional collaboration

Education

Degree in Computer Science / AI / Data Science / Software Engineering / Information Systems

Tools

Databricks

MLflow

AWS

Apache Spark

Delta Lake

Terraform

Docker

Kubernetes

Airflow

Job description

Key Responsibilities

• Design and implement MLOps/AIOps platform components for experiment tracking, model deployment, monitoring and retraining using Databricks, MLflow and AWS.
• Productionise ML/AI prototypes into scalable, tested and maintainable pipelines, APIs and model-serving services.
• Build and maintain feature engineering pipelines using Databricks, PySpark, SQL and AWS.
• Develop automated CI/CD/CT workflows for code, data, models and infrastructure deployment.
• Implement model versioning, experiment tracking, data lineage, reproducibility and governance controls.
• Monitor system health, model performance, data quality, drift, resource utilisation and cloud costs.
• Optimise Spark workloads, data pipelines and model-serving infrastructure for performance and reliability.
• Establish model evaluation, regression testing, A/B testing and deployment rollback processes.
• Implement cloud security, access controls, audit logging and Responsible AI safeguards.
• Support recommendation and search systems through model maintenance, retraining, experimentation and performance optimisation.
• Maintain technical documentation, operational runbooks and deployment records, and support knowledge transfer across teams.

Requirements & Qualifications

• Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related field.
• Typically 4-7 years of relevant software, data or ML engineering experience, with hands-on experience deploying and operating production ML/AI systems.
• Strong Python programming and software engineering skills, including API development, automated testing, Git and modular design.
• Hands-on experience with Databricks and MLflow for experiment tracking, model registry, workflow orchestration or model serving.
• Strong proficiency in PySpark, Apache Spark and SQL, including distributed data processing and pipeline optimisation.
• Experience with Databricks, Delta Lake and AWS S3 for production data pipelines and lakehouse architectures.
• Experience with feature engineering, data validation, model evaluation, deployment, monitoring and retraining.
• Familiarity with AWS services such as Glue, IAM, Lambda, Step Functions, CloudWatch and related cloud services.
• Experience with Docker, CI/CD tools and infrastructure-as-code solutions such as Terraform.
• Knowledge of model monitoring, drift detection, reproducibility, rollback procedures and cloud security.
• Strong problem-solving, communication and cross-functional collaboration skills.

Preferred Skills

• Experience with Unity Catalog, Delta Live Tables, Lakeflow or feature stores.
• Familiarity with Kubernetes, Airflow, Kafka, AWS MSK/Kinesis or event-driven architectures.
• Experience productionising generative AI or agentic AI applications.
• Knowledge of OpenTelemetry, Prometheus, Grafana, A/B testing or champion-challenger deployment.
• Experience supporting recommendation engines, search systems or high-volume AI services.
• Familiarity with Singapore public-sector security, governance and Responsible AI requirements.