We are seeking a highly skilled Senior GenAI Platform Engineer who can independently manage the full lifecycle of Generative AI (GenAI) solution development – from model selection and fine‑tuning to backend integration and production deployment. This role requires a hybrid skillset across GenAI solution engineering, backend development, and Site Reliability Engineering (SRE), ensuring robust, scalable, and reliable GenAI solutions fully integrated with core banking and enterprise systems.
Mandatory Skill Set
- 3–5 years of hands‑on experience in developing, fine‑tuning, and deploying GenAI / LLM applications (OpenAI, Anthropic, Gemini, Llama, etc.)
- Experience with RAG pipelines and frameworks such as LangChain, LlamaIndex, or Hugging Face Transformers
- Hands‑on experience with Docker, Kubernetes, and Terraform for containerization, orchestration, and infrastructure automation
- Experience with LLMOps and SRE principles – reliability, scalability, monitoring, and performance optimization
- Proven experience implementing CI / CD pipelines (GitLab CI, Jenkins, or similar tools)
- Strong backend development skills using Python, Golang, or Node.js for APIs and microservices
Desired Skill Set
Experience in cloud environments such as AWS, GCP, or AzureFamiliarity with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus) for context retrievalWorking knowledge of banking system integrations, especially core banking or CRM platformsExposure to API Gateway management and secure data exchange protocolsBackground in CI / CD pipeline design using GitLab CI, Jenkins, or similar toolsResponsibilities
Lead the end‑to‑end design, development, and deployment of GenAI solutions integrated with enterprise systemsBuild and optimize RAG pipelines and backend services powering GenAI capabilitiesDevelop microservices and APIs to integrate AI functionalities within core banking or enterprise applicationsDesign and implement LLM observability and performance monitoring frameworksAutomate and manage infrastructure through Terraform, Docker, and KubernetesSet up and maintain Model Context Protocol (MCP) servers for efficient context streamingImplement LLMOps best practices for continuous monitoring, versioning, and retraining of modelsDefine and manage CI / CD pipelines for infrastructure, backend code, and GenAI modelsEnsure system reliability, scalability, and cost optimization across environmentsCollaborate cross‑functionally with business, data, and IT teams to ensure smooth solution deliveryLocation : Kuala Lumpur City Centre, Kuala Lumpur, MY
#J-18808-Ljbffr