Machine Learning Engineer

Responsibilities:

-     Design, train and productionise supervised, unsupervised and deep-learning models that power fraud detection, risk scoring and personalised client experiences.

-     Build repeatable ML pipelines in Python (TensorFlow / PyTorch / scikit-learn) and Java/Scala, orchestrated in Vertex AI, Azure ML and on-prem GPU clusters.

-     Implement full-cycle MLOps—version control, automated testing, CI/CD, model registry, drift monitoring and rollback—using Git/Bitbucket, Terraform, Docker and Kubernetes (GKE ).

-     Serve low-latency inference via containerised micro-services and streaming engines (Flink, Spark Structured Streaming, Pub/Sub / Kafka)

-     Engineer and manage a cross-cloud feature store, integrating BigQuery and on-prem RDBMS to guarantee consistent training/serving features.

-     Conduct robust experimentation (A/B, champion-challenger) and hyper-parameter tuning to optimise model performance and business KPIs.

-     Embed responsible-AI controls—bias detection, explainability, encryption, role-based access—aligned with UU PDP & OJK AI governance.

-     Produce audit-ready documentation, model cards, and lineage artifacts for regulators and internal risk committees.

-     Partner with product, data engineering and compliance teams to translate complex ML outputs into actionable insights and secure APIs.

-     Track emerging techniques (LLMs, Gen AI, vector databases) and recommend adoption paths that fit latency, cost and compliance constraints.

Qualifications:

-     3-5 years building and deploying ML solutions at scale; experience in regulated finance a plus.

-     Bachelors in Computer Science, Statistics or related field; Masters/PhD welcomed.

-     Advanced Python plus proficiency in Java/Scala; expert SQL for feature engineering on big-data platforms.

-     Hands-on with Vertex AI, BigQuery ML, and on-prem GPU or Hadoop ecosystems.

-     Proven MLOps skills—GitOps, CI/CD pipelines, Docker/K8s, Terraform Helm charts, model monitoring (Prometheus/Grafana).

-     Solid grasp of statistics, optimisation and experimental design; able to tune and validate models against drift and performance KPIs.

-     Familiarity with data-ethics frameworks, bias mitigation, and privacy-preserving ML techniques demanded by UU PDP & OJK regulations.

-     Experience integrating real-time scoring services with Spark or Flink streams and REST/gRPC endpoints.

-     Excellent communication skills to bridge technical depth with executive, product and regulatory stakeholders.

Information :

• Company : Inspira Multi Technology
• Position : Machine Learning Engineer
• Location : Jakarta
• Country : ID