{"id":824050,"url":"https://alion.io/job/ncs-eg-ai-engineer-autonomous-network","title":"#EG AI Engineer (Autonomous Network)","company":{"id":8907,"name":"NCS","domain":"ncs.co","url":"https://alion.io/company/ncs-co","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":82,"open_postings":376,"ghost_share":0.298,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":45000,"max_usd":116000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"Braintrust","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepSeek","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LiteLLM","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"Mistral","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PoC Library","optional":false},{"name":"Portkey","optional":false},{"name":"Prompt Caching","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Red Teaming","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"ServiceNow","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Apache Pulsar","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Grafana","optional":true},{"name":"ITSM","optional":true},{"name":"LangSmith","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prometheus","optional":true},{"name":"Vertex AI","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-06-09T06:24:41Z","employer_posted_date":"2026-06-09","last_verified_at":"2026-10-17T21:17:02Z","board_verified":true,"closed_at":null,"days_open":130,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":129},"description":"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.\n The AI Engineer builds, tests, and iterates production Agentic AI systems at the core of the Autonomous Networks Programme. Working directly with the AI Solution Architect, Business Analyst, and Central Infrastructure Solution Architect, this role turns architecture designs into working code - implementing agent logic, integrating OSS APIs, building RAG pipelines, and deploying agents into staging and production environments.\nThis is a hands-on engineering and development role where code quality, test rigour, and operational reliability are the primary measures of success.\nWhat will you do?\nTechnical Delivery\nOwn end-to-end delivery of AI workstreams: from requirements through data preparation, modelling, integration, testing, and production handover\nDevelop agentic applications including RAG pipelines, prompt-engineered agents, and agentic workflows using LangChain, LlamaIndex, LangGraph, or plain Python\nBuild on top of GenAI application stacks including LLM orchestration, observability (Langfuse, Braintrust), guardrails, and LLM gateway patterns (LiteLLM, Portkey)\nImplement multi-agent orchestration layer: event routing, resource locking, inter-agent handoff contracts, prompt caching, and shared state management\nImplement agentic design patterns including workflow evaluation, LLM-as-Judge, and AI red teaming\nAdopt Model Context Protocol (MCP) and Agent-to-Agent (A2A) Protocol as foundational extension mechanisms, enabling agents to operate safely across enterprise ecosystems\nWork with AI Solution Architect and Business Analysts on benchmarking and golden-set compilation for agent evaluation\nImplement effective RAG architectures: chunking strategies, embedding selection, vector store configuration (Qdrant, Milvus, pgvector), hybrid search, and reranking\nBuild and maintain evaluation harnesses measuring correctness, faithfulness, latency, and safety. Run continuous LLM-as-Judge evaluation on production traces\nWrite clean, maintainable Python; participate in code reviews; contribute to shared AI platform libraries\nOSS Integration & Data Engineering\nImplement REST, NETCONF/YANG, SNMP trap ingestion, and gNMI streaming telemetry integrations connecting agents to NMS and EMS systems\nBuild ServiceNow integrations for ticket read/write, triage updates, change record queries, and human-in-the-loop approval workflow triggers for the Execution Agent\nBuild the alarm and telemetry data normalisation pipeline converting multi-vendor OSS data into agent-consumable schemas\nImplement Kafka or equivalent event streaming consumers delivering real-time alarm data to the Alarm Correlation and Ticket Triaging agents\nWork with data and integration engineers to normalise multi-vendor, multi-format network telemetry into consistent schemas the agents can reason over\nSafety, Guardrails & Risk\nImplement guardrail architectures: hard limits, soft limits with human confirmation flows, blast radius controls, and escalation logic\nAssist the AI Solution Architect to assess blast radius for each agent - documenting worst-case impacts and designing specific mitigating controls\nImplement and validate rollback procedures for all agent-initiated network actions, including automated rollback triggers based on post-execution KPI degradation\nWork with QA engineers to design and test solutions via defined test cases, and review required improvements\nEnsure all agent architectures comply with IMDA regulatory requirements, AIVerify framework requirements, InfoSec policies, and CMB change governance frameworks\nObservability & Evaluation\nImplement the Decision Audit Trail schema for all agents ensuring every decision is fully traceable, explainable, and available for post-incident review\nBuild the Agent Operations Dashboard for the NOC team: real-time agent status, decision volumes, accuracy metrics, and exception alerts\nDefine and implement the evaluation framework: factual, reasoning, tool-call, retrieval and planning accuracy, confidence thresholds, and human feedback capture\nImplement continuous evaluation of production agent traces using LLM-as-Judge for quality and safety; design human evaluation sample process\nBuild drift detection mechanisms that alert the team when agent output quality degrades before it becomes an operational incident\nContainerise and deploy agent services using Docker and CI/CD pipelines; manage staging and production deployments with rollback capability\nThe ideal candidate should possess:\nMinimum 3 years of AI/ML engineering experience, with at least 1 year in production Agentic AI systems (not PoC or prototype-only)\nDemonstrable experience building AI agents that call external tools, manage state, and operate in production environments with real operational consequences\nHands-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\nExperience building RAG pipelines with vector databases (Qdrant, Milvus, Pinecone, Weaviate, pgvector) in production, including hybrid search and reranking\nPractical experience with at least one agentic AI framework: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or custom tool-calling\nStrong Python - modular, testable, production-quality code. Proficiency with Git, CI/CD, Docker, and LLMOps practices\nFull-stack awareness: REST APIs, front-end integration, data pipelines\nDegree in Computer Science, Electrical Engineering, AI/ML, or related technical field\nHighly Desirable\nExperience with Telco, utilities, or other 24×7 critical infrastructure environments where AI system failures have direct operational consequences\nFamiliarity with OSS systems: NMS, EMS, network alarm management, NETCONF/YANG, or gNMI streaming telemetry\nExperience with event streaming platforms: Apache Kafka, Pulsar, or equivalent\nExperience integrating with ServiceNow REST API for ITSM data\nExperience with AI observability tools: Langfuse, Braintrust, LangSmith, Weights & Biases, Prometheus/Grafana\nExperience implementing AI systems subject to regulatory or formal risk governance frameworks (CMB, InfoSec review, audit requirements)\nFamiliarity with network automation platforms (Cisco NSO, Nokia CloudBand, ONAP) as agent integration targets\nFamiliarity with TM Forum frameworks including the Autonomous Network Maturity Model (L0-L5)\nCloud ML platform experience: AWS SageMaker, Azure ML, or Google Vertex AI\nQualifications & Certifications\nRequired: Degree in Computer Science, Electrical Engineering, AI/ML, or related technical field\n Desirable: Professional certifications in Agentic AI development and architecting\nWe 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.\nTogether, we make the extraordinary happen.\nLearn more about us at ncs.co and visit our LinkedIn career site.\nScam Alert\nWe are aware of fraudulent job offers and impersonations of NCS recruiters. Phishing emails using convincing-looking but fake addresses are also commonly used to trick you into thinking that they come from official NCS sources.\nPlease note that all official communications from NCS Group will only be sent from verified corporate email addresses. Always check that the sender’s email address ends with the genuine NCS domain, @ncs.com.sg and beware of extra letters, symbols or misspellings. 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