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Machine Learning Engineer

Machine Learning Engineer

Ecloudvalley Technology Sdn BhdMalaysia
5 hours ago
Job description

As a GenAI Solutions Engineer (MLOps-leaning) at eCloudvalley, you will turn AI prototypes into secure, observable, and cost-efficient production solutions on AWS. You'll collaborate closely with the Senior Cloud Architect, product / PS teams, and customers to build RAG / KB chat, voice assistants, and AI integrations—complete with evaluation loops, guardrails, and CI / CD.

Job Responsibilities :

  • Design, implement, and operate serverless GenAI applications on AWS (Bedrock, Lambda, API Gateway, DynamoDB, S3, Step Functions, EventBridge, SQS).
  • Productionize Bedrock Knowledge Bases / RAG flows and voice / chat assistants with clear SLIs / SLOs.
  • Build evaluation frameworks (quality / latency / cost), and run scheduled regressions with dashboards.
  • Implement guardrails & safety : PII redaction, content filtering, prompt hardening, audit logs, red-team tests.
  • Establish observability by default : structured logging, metrics, alarms, and runbooks.
  • Own cost management : infra budgeting, caching strategies, usage caps, FinOps tagging / alerts.
  • Ship CI / CD pipelines (GitLab CI / GitHub Actions) and reusable Terraform modules for repeatable delivery.
  • Collaborate on reviews (IAM / KMS, privacy, compliance) and incident readiness; support sales / PS with demos and workshops.
  • Document architectures, playbooks, and "paved road" templates.
  • Familiar in Data Pipeline / ETL – S3 + Glue + Athena + Step Function

Job Requirements :

  • Education / Experience : Bachelor's in CS / EE / SE (or equivalent). 3+ years on public cloud; 2+ years with AWS serverless or data / ML pipelines.
  • Cloud & Security : AWS fundamentals—VPC / IAM / KMS, CloudWatch
  • GenAI Platforms : Practical experience with AWS Bedrock (Models / KB / Guardrails) or equivalent LLM platforms.
  • Application Eng. : Python (boto3) and / or ; API design, testing, troubleshooting.
  • RAG & Data : Embeddings, chunking, retrieval tuning; OpenSearch or Aurora pgvector (or similar).
  • MLOps for GenAI : Build eval harnesses (quality / latency / cost), A / B tests, monitor model / prompt changes.
  • Integrations : Programmable Voice / SMS or Amazon Connect, webhooks, OAuth / OIDC
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    Machine Learning Engineer • Malaysia