Maybank AI - GenAI Architect at Capgemini Portugal

Kuala Lumpur, Kuala Lumpur, Malaysia -

Full Time

Start Date

Immediate

Expiry Date

20 Jul, 26

Salary

0.0

Posted On

21 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

GenAI, LLM, RAG, Agentic AI, Python, TypeScript, Azure, AWS, GCP, Vector Databases, MLOps, LLOps, Solution Architecture, Prompt Engineering, Data Governance, Stakeholder Management

Industry

IT Services and IT Consulting

Description

Long Description Key ResponsibilitiesStrategy & Discovery• Partner with business, product, and portfolio leaders to identify high-ROI GenAI and agentic use cases (knowledge work automation, decision support, customer service agents, code assistants, risk monitoring, etc.).• Run AI readiness assessments: data landscape, governance, model options, risk and regulatory constraints.• Produce solution blueprints and adoption roadmaps aligned to enterprise architecture and target operating model.Architecture & Design• Design end-to-end AI architectures: prompt/flow orchestration, RAG pipelines, multi-agent systems (planner/executor/critic), tool ecosystems (search, DB, APIs), vector stores, guardrails, observability, and CI/CD for ML.• Select and integrate LLM providers (e.g., Azure OpenAI, Bedrock, Vertex, Anthropic) with model evaluation criteria (accuracy, latency, cost, safety).• Define data pipelines for embeddings, chunking, metadata, and governed retrieval (role-based access, PII handling, geo-compliance).• Architect safety & trust: content filtering, PII redaction, policy enforcement, jailbreak protection, and Responsible AI patterns.• Plan for scalability, performance, and cost (caching, batching, streaming, quantization, distillation, serverless/containerized deployment).Delivery & Engineering Leadership• Lead engineers to implement agent frameworks (e.g., LangChain, Semantic Kernel, LlamaIndex, LangGraph) and workflow orchestration (e.g., Airflow, Durable Functions).• Establish evaluation harnesses: golden sets, rubric scoring, hallucination tests, toxicity/PII metrics, regression suites.• Drive MLOps/LLOps: versioning of prompts/flows, model registries, monitoring, drift detection, and feedback loops for continuous improvement.• Ensure integration with enterprise systems (CRM, ERP, data lakes, APIs) and DevSecOps standards.Governance, Risk & Compliance• Implement Responsible AI and model risk management: documentation, auditability, exception management.• Align with regional regulations and industry frameworks (e.g., PDPA, GDPR, financial services guidelines).• Define human in the loop (HITL) and escalation paths for critical decisions.Stakeholder Management & Change• Translate complex AI concepts into business friendly narratives, TCO models, and OKR/KPI frameworks.• Conduct enablement: playbooks, demos, training, and adoption programs for business units.• Build vendor and partner relationships; evaluate POCs and coordinate pilots to production.Long Description Required Qualifications• Bachelor’s/Master’s in Computer Science, Data/AI, Engineering, or related field (or equivalent experience).• 7–12+ years in solution architecture, ML/AI engineering, or platform engineering, with 2–4+ years hands on GenAI/LLM solutions.• Proven delivery of production GenAI systems (RAG, tool use, agents) at enterprise scale.• Strong knowledge of:o LLMs & Embeddings: model families, context management, fine tuning/adapter methods, prompt engineering.o Agentic AI: planners, executors, memory, tool routing, multi-agent collaboration, safety and oversight.o Data & Infra: vector DBs (CosmosDB, Pinecone, Redis, PgVector, Azure AI Search), data lakes/warehouses, microservices, APIs, containers (Docker/K8s), serverless.o Cloud: Azure, AWS, or GCP—identity, networking, secrets, observability, and cost control.o MLOps/LLOps: model/prompt versioning, A/B testing, monitoring, evaluation pipelines.• Excellent communication, stakeholder engagement, and consultative problem solving skills.Preferred (Nice to Have)• Experience with Semantic Kernel, LangChain, LlamaIndex, LangGraph or custom orchestration libraries.• Evaluation & Safety tooling: prompt injection detectors, redaction, policy engines.• Experience with domain compliance (financial services, telco, healthcare, public sector).• Hands-on with vectorization strategies, multilingual retrieval, and knowledge graph augmentation.• GenAI UX experience: conversational design, guardrails in UI, user feedback instrumentation.• Publications, patents, or OSS contributions in GenAI/agent systems.Core Skills MatrixTechnical• LLMs (open & closed source), embeddings, RAG, multi-agent design, tool calling.• Python/TypeScript; API design; orchestration; CI/CD; cloud services; observability.• Vector databases, chunking strategies, metadata & relevance tuning.• Security & Responsible AI: content moderation, PII handling, policy controls.Consulting & Leadership• Use case discovery, value cases, ROI/TCO modeling.

Responsibilities

The GenAI Architect will partner with business leaders to identify high-ROI AI use cases and design end-to-end architectures including RAG pipelines and multi-agent systems. They will also lead engineering teams in implementing agent frameworks while ensuring compliance with enterprise governance, risk, and safety standards.