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GenAI

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Onsite

GenAI

SPI GLOBAL CONTENT HOLDING PTE. LTD.

GenAI

Skills

Google Cloud PlatformDockerPythonPyTorchTensorFlowMachine LearningAIAWSKubernetesCRMComplianceRegulatory ComplianceAzure

5 days ago

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Important information


Contract type:

Permanent, Temporary

Location:

Singapore

Work mode:

Onsite

Published on:

22 October 2026

What they need


Greetings !!!

We’re Hiring | Generative AI Technical Lead

Domain: BFS / BFSI (Preferred)

Experience: 10+ years (3+ years in GenAI / LLM architecture)

Role Summary

We are looking for a Generative AI Technical lead /Engineer , the end-to-end design, and governance of enterprise-grade GenAI solutions. This role will act as the principal technical authority driving scalable, secure, and responsible AI implementations across the organization.

Key Responsibilities

Define GenAI architecture blueprints, reference architectures, and technology standards

Design and implement RAG pipelines, agentic workflows, and model fine-tuning frameworks

Hands-on development of production-grade GenAI applications

Evaluate and select LLMs, vector databases, and orchestration frameworks

Architect integrations with enterprise platforms (CRM, ERP, HRIS, data platforms)

Optimize performance, latency, and GPU/TPU cost efficiency

Implement Responsible AI governance (bias mitigation, XAI, toxicity filtering, data provenance)

Lead LLMOps practices – deployment, monitoring, versioning, CI/CD

Drive GenAI innovation roadmap and emerging technology pilots

Act as GenAI SME for C-level stakeholders and business leaders

Technical Expertise Required

10+ years in Technical Architecture / Data / ML Engineering

3+ years designing production-grade GenAI solutions

Deep expertise in LLMs, Transformers, RAG, prompt engineering

Strong experience with AWS / Azure / GCP (Bedrock, Azure OpenAI, Vertex AI)

Proficiency in Python and ML libraries (PyTorch, TensorFlow)

Hands-on with vector databases (OpenSearch, Pinecone, Weaviate, etc.)

Strong knowledge of containerization & orchestration (Docker, Kubernetes)

Experience with data pipelines, embedding, and modern data architectures

Strong understanding of security, privacy, and regulatory compliance (GDPR)

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