#EG Agentic AI Engineer (AI Tech Lead)

NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group.

What will you do?

Technical Delivery

• Own end-to-end delivery of AI workstreams: from requirements through data preparation, modelling, integration, testing, and production handover
• Develop agentic applications including RAG pipelines, prompt-engineered agents, and agentic workflows using LangChain, LlamaIndex, LangGraph, or plain Python
• Build on top of GenAI application stacks including LLM orchestration, observability (Langfuse, Braintrust), guardrails, and LLM gateway patterns (LiteLLM, Portkey)
• Implement multi-agent orchestration layer: event routing, resource locking, inter-agent handoff contracts, prompt caching, and shared state management
• Implement agentic design patterns including workflow evaluation, LLM-as-Judge, and AI red teaming
• Adopt Model Context Protocol (MCP) and Agent-to-Agent (A2A) Protocol as foundational extension mechanisms, enabling agents to operate safely across enterprise ecosystems
• Work with AI Solution Architect and Business Analysts on benchmarking and golden-set compilation for agent evaluation
• Implement effective RAG architectures: chunking strategies, embedding selection, vector store configuration (Qdrant, Milvus, pgvector), hybrid search, and reranking
• Build and maintain evaluation harnesses measuring correctness, faithfulness, latency, and safety. Run continuous LLM-as-Judge evaluation on production traces
• Write clean, maintainable Python; participate in code reviews; contribute to shared AI platform libraries

OSS Integration & Data Engineering

• Implement REST, NETCONF/YANG, SNMP trap ingestion, and gNMI streaming telemetry integrations connecting agents to NMS and EMS systems
• Build ServiceNow integrations for ticket read/write, triage updates, change record queries, and human-in-the-loop approval workflow triggers for the Execution Agent
• Build the alarm and telemetry data normalisation pipeline converting multi-vendor OSS data into agent-consumable schemas
• Implement Kafka or equivalent event streaming consumers delivering real-time alarm data to the Alarm Correlation and Ticket Triaging agents
• Work with data and integration engineers to normalise multi-vendor, multi-format network telemetry into consistent schemas the agents can reason over

Safety, Guardrails & Risk

• Implement guardrail architectures: hard limits, soft limits with human confirmation flows, blast radius controls, and escalation logic
• Assist the AI Solution Architect to assess blast radius for each agent - documenting worst-case impacts and designing specific mitigating controls
• Implement and validate rollback procedures for all agent-initiated network actions, including automated rollback triggers based on post-execution KPI degradation
• Work with QA engineers to design and test solutions via defined test cases, and review required improvements
• Ensure all agent architectures comply with IMDA regulatory requirements, AIVerify framework requirements, InfoSec policies, and CMB change governance frameworks

Observability & Evaluation

• Implement the Decision Audit Trail schema for all agents ensuring every decision is fully traceable, explainable, and available for post-incident review
• Build the Agent Operations Dashboard for the NOC team: real-time agent status, decision volumes, accuracy metrics, and exception alerts
• Define and implement the evaluation framework: factual, reasoning, tool-call, retrieval and planning accuracy, confidence thresholds, and human feedback capture
• Implement continuous evaluation of production agent traces using LLM-as-Judge for quality and safety; design human evaluation sample process
• Build drift detection mechanisms that alert the team when agent output quality degrades before it becomes an operational incident
• Containerise and deploy agent services using Docker and CI/CD pipelines; manage staging and production deployments with rollback capability

The ideal candidate should possess:

• Minimum 3 years of AI/ML engineering experience, with at least 1 year in production Agentic AI systems (not PoC or prototype-only)
• Demonstrable experience building AI agents that call external tools, manage state, and operate in production environments with real operational consequences
• Hands-on experience with LLM APIs (OpenAI, Anthropic Claude) or Open Source LLMs on vLLM (Qwen, Mistral, DeepSeek) in production - including prompt engineering, evaluation, and cost management
• Experience building RAG pipelines with vector databases (Qdrant, Milvus, Pinecone, Weaviate, pgvector) in production, including hybrid search and reranking
• Practical experience with at least one agentic AI framework: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or custom tool-calling
• Strong Python - modular, testable, production-quality code. Proficiency with Git, CI/CD, Docker, and LLMOps practices
• Full-stack awareness: REST APIs, front-end integration, data pipelines
• Degree in Computer Science, Electrical Engineering, AI/ML, or related technical field

Highly Desirable

• Experience with Telco, utilities, or other 24×7 critical infrastructure environments where AI system failures have direct operational consequences
• Familiarity with OSS systems: NMS, EMS, network alarm management, NETCONF/YANG, or gNMI streaming telemetry
• Experience with event streaming platforms: Apache Kafka, Pulsar, or equivalent
• Experience integrating with ServiceNow REST API for ITSM data
• Experience with AI observability tools: Langfuse, Braintrust, LangSmith, Weights & Biases, Prometheus/Grafana
• Experience implementing AI systems subject to regulatory or formal risk governance frameworks (CMB, InfoSec review, audit requirements)
• Familiarity with network automation platforms (Cisco NSO, Nokia CloudBand, ONAP) as agent integration targets
• Familiarity with TM Forum frameworks including the Autonomous Network Maturity Model (L0-L5)
• Cloud ML platform experience: AWS SageMaker, Azure ML, or Google Vertex AI

Qualifications & Certifications

• Required: Degree in Computer Science, Electrical Engineering, AI/ML, or related technical field
• Desirable: Professional certifications in Agentic AI development and architecting

Why Join NCS

• Lead high-impact AI management consulting programs for major enterprises and public sector clients.

• Shape enterprise strategies and governance frameworks that drive real transformation.

• Work with a talented, multidisciplinary team in a collaborative environment.

• Competitive compensation and strong professional development support.

We are driven by our AEIOU beliefs-Adventure, Excellence, Integrity, Ownership, and Unity-and we seek individuals who embody these values in both their professional and personal lives. We are committed to our Impact: Valuing our clients, Growing our people, and Creating our future.

Together, we make the extraordinary happen.

Learn more about us at ncs.co and visit our LinkedIn career site.

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