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CAREER OPPORTUNITIES > MALAYSIA

Forward Deployed AI Engineer (FDE) – AI Engineering & Operations

The Forward Deployed AI Engineer (FDE) is the hands-on software engineer who embeds on-site with clients to deploy sovereign Enterprise AI directly into their environments. You write custom code, build real-time data pipelines, connect AI agent workflows — powered by the latest open-source models (e.g., DeepSeek series) — into legacy client databases, and ensure the local AI engine runs securely on the client's private infrastructure.

This role is the critical bridge between Apexone's Enterprise AI capabilities and client delivery. Unlike traditional AI vendors that rely on public cloud APIs, FDEs operate strictly within client firewalls to ensure absolute compliance with Malaysia PDPA, Oman PDPL, GDPR, and local data sovereignty laws. You will work across Apexone's flagship platforms — Digital Banking, Apexium CEX, Universal Loyalty Ecosystem, Smart Manufacturing, and Smart City — deploying AI that delivers measurable business outcomes.

You will build working proof-of-concept (PoC) workflows within weeks on-site, then refactor them into hardened, low-latency production pipelines. This role requires a unique blend of full-stack software engineering, AI integration expertise, and client-facing delivery discipline.

The Technical Builder

LOCATION

DEPARTMENT

REPORTS TO

One City, Malaysia (with regional travel as required)

AI Engineering & Operations

Chief Technology Officer (CTO)

Core Responsibilities

Key Role Objectives:
A. On-Site Data Ingestion & Pipeline Building

  • Legacy Data Integration: Connect messy client enterprise data sources — SQL ledgers, unstructured PDFs, internal APIs, legacy databases, and flat files — into local vector stores (Qdrant, Milvus, pgvector) and secure AI context windows.

  • Data Pipeline Architecture: Build robust, maintainable data ingestion pipelines that handle diverse data formats, schemas, and volumes with minimal latency.

  • ETL/ELT for AI: Design and implement ETL/ELT workflows that transform raw client data into AI-ready formats — including chunking, embedding generation, and metadata enrichment.

  • Real-Time Data Streaming: Implement real-time data streaming pipelines for time-sensitive applications — such as fraud detection, trade monitoring, and transaction validation.

  • Data Quality & Validation: Implement data quality checks, validation rules, and anomaly detection to ensure AI models receive clean, accurate data.

  • Documentation & Knowledge Transfer: Document data pipelines, schemas, and integration patterns to enable client teams to maintain and evolve systems independently.

B. Custom Agent & Tool Integration

  • Agent Workflow Development: Build custom REST/gRPC wrappers and tool-calling scripts so AI agents can safely trigger real-world business actions — verifying bank ledgers, updating loyalty point accounts, flagging suspicious trades, generating compliance reports, or triggering production alerts.

  • Tool-Calling & Action Registry: Design and implement secure tool-calling frameworks that allow AI agents to interact with client systems through controlled, auditable actions.

  • Legacy System Integration: Connect AI agents to legacy client systems — including ERP, SCM, WMS, CRM, SCADA, payment gateways, and core banking systems — through secure API integrations.

  • Action Validation & Guardrails: Implement validation layers and safety guardrails that prevent AI agents from executing unauthorized or harmful actions.

  • Agent Observability: Build monitoring and logging frameworks that provide visibility into agent decisions, actions, and outcomes — enabling troubleshooting and compliance audits.

C. Private / On-Premises Deployment

  • Containerized Deployment: Configure private containers (Docker / Kubernetes) and localized GPU runtimes (vLLM / Ollama) directly inside the client's firewall to comply with regional data sovereignty regulations.

  • Zero Data Leakage: Ensure all AI workloads operate within client-controlled infrastructure with zero data leakage to public clouds or external systems.

  • Infrastructure as Code: Deploy infrastructure using Terraform and Kubernetes manifests, enabling reproducible, auditable, and version-controlled deployments.

  • GPU Runtime Configuration: Configure and optimize GPU runtimes for client hardware — including NVIDIA H100/A100, AMD GPUs, or Huawei Ascend NPU — ensuring optimal performance for inference workloads.

  • Security Hardening: Implement security best practices — including network segmentation, role-based access control, encrypted model weights, and secure API endpoints.

  • Forward Deployed Enablement: Build deployment templates, runbooks, and tooling that enable future FDE teams to deploy rapidly and consistently.

D. Rapid Prototyping to Production

  • Proof-of-Concept (PoC) Development: Build working proof-of-concept workflows within weeks on-site, demonstrating value to clients quickly and building momentum for full-scale deployment.

  • Production Refactoring: Refactor PoC workflows into hardened, low-latency production pipelines — including error handling, retry logic, monitoring, alerting, and scaling.

  • Performance Optimization: Optimize pipelines for latency, throughput, and cost — ensuring production systems meet client SLAs (e.g., <100ms inference latency, 99.9% uptime).

  • CI/CD for AI: Implement CI/CD pipelines for model deployment and updates, enabling seamless rollouts with automated testing and validation.

  • Client Handover: Conduct knowledge transfer sessions and produce comprehensive documentation to enable client teams to operate and maintain deployed systems independently.

E. Client Technical Partnership

  • On-Site Engagement: Embed directly within client teams, building trust, understanding operational context, and delivering value through close collaboration.

  • Technical Advisory: Provide technical guidance to client teams on AI integration, data pipeline design, and infrastructure optimization.

  • Requirements Translation: Work closely with Technical Product Managers and Solution Architects to translate client requirements into technical specifications and implementation plans.

  • Issue Resolution: Troubleshoot and resolve technical issues in production environments — including data quality issues, pipeline failures, model performance degradation, and infrastructure problems.

  • Client Training: Train client technical teams on deployed systems, enabling them to independently operate, maintain, and evolve AI capabilities.

F. On-Call Production Support

  • Production Monitoring: Participate in on-call rotation to monitor production AI systems, ensuring continuous operation and rapid incident response.

  • Incident Management: Lead incident response for production issues — including root cause analysis, resolution, and post-incident reporting.

  • Continuous Improvement: Identify and implement improvements to production systems based on incident learnings, performance data, and client feedback.

Required Qualifications & Experience:

  • 5+ years of software engineering experience with strong full-stack capabilities

  • Full-stack software engineering expertise in Python, TypeScript/Node.js, and/or Go

  • API development experience — building REST/gRPC APIs, webhook integrations, and secure service-to-service communication

  • Vector Database experience — Qdrant, Milvus, pgvector, or similar — including deployment, configuration, and performance optimization

  • LLM framework experience — LangChain, LangGraph, or similar — including agent development, tool-calling, and RAG pipeline construction

  • Linux/Docker expertise — containerization, orchestration, and system administration

  • Private cloud deployment experience — Kubernetes, private cloud infrastructure, and on-premises deployment patterns

  • Strong debugging and troubleshooting skills — ability to diagnose issues across complex distributed systems

  • Excellent communication skills — ability to explain technical concepts to non-technical stakeholders and build trust with client teams

  • Preferred Qualifications:

    • Experience with sovereign AI deployments in regulated industries (FinTech, Banking, Government, Healthcare)

    • Experience with 01.AI, DeepSeek, Llama, or similar open-source model ecosystems

    • Experience with data pipeline tools — Apache Airflow, dbt, Prefect, or similar

    • Experience with FinTech/Banking systems — core banking, payment systems, ledgers, compliance systems

    • Experience with industrial systems — SCADA, PLCs, MES, or similar operational technology

    • Experience with cloud platforms — AWS, Azure, Alibaba, Huawei, GCP — and hybrid cloud architectures

    • Experience with monitoring tools — Prometheus, Grafana, Datadog, or similar

    • Certifications: AWS/Azure/Alibaba/Huawei/GCP certification, Kubernetes certification (CKA/CKAD) or relevant AI/ML certifications

Success Criteria in First 12 Months:

  • Successfully deploy at least 2-3 production AI systems on client private infrastructure with 99.9% uptime and <100ms latency

  • Build data pipelines integrating at least 3 distinct client data sources (SQL, PDFs, APIs) into vector stores for AI ingestion

  • Establish agent tool-calling frameworks enabling AI agents to execute at least 5 distinct business actions (e.g., ledger verification, loyalty updates, trade flagging)

  • Refactor at least 2 PoC workflows into hardened production pipelines with CI/CD, monitoring, and alerting

  • Build deployment templates and runbooks enabling rapid deployment for future FDE engagements

  • Achieve high client satisfaction (measured through NPS or similar) on at least 2 on-site engagements

  • Successfully conduct knowledge transfer and training for at least 2 client teams, enabling independent operation of deployed systems

  • Establish on-call production support framework with incident response procedures and continuous improvement processes

  • Contribute to at least 1 repeatable deployment pattern or internal tool that accelerates future FDE deployments

EMPLOYMENT TYPE: FULL-TIME, PERMANENT

APPLICATION PROCESS

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Our recruitment process: CV Review → Technical Assessment → Leadership Interview → Offer

Apexone is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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