{"id":824103,"url":"https://alion.io/job/ncs-eg-ai-engineer-contact-centre","title":"#EG AI Engineer (Contact Centre)","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":"A","score":91,"open_postings":342,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":67,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","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":38000,"max_usd":75000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"JavaScript","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"pgvector","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"TypeScript","optional":false},{"name":"DeepEval","optional":true},{"name":"Deepgram","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"MLFlow","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prefect","optional":true},{"name":"Promptfoo","optional":true},{"name":"Text-to-Speech","optional":true},{"name":"Twilio","optional":true},{"name":"Weights & Biases","optional":true},{"name":"Whisper","optional":true}],"status":"live","first_seen_at":"2026-04-28T06:46:32Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-25T23:45:58Z","board_verified":true,"closed_at":null,"days_open":180,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":179},"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 As an AI Engineer who bridges traditional machine learning and modern generative / agentic AI. This role involves designing, building, and deploying end-to-end AI solutions including classical ML models, RAG-based applications, and lightweight agentic workflows. The successful candidate will contribute hands-on to client delivery projects, applying strong Python engineering and statistical rigor to develop scalable AI solutions that work reliably in production environments.\nWhat will you do?\nAI / ML Model Development & Deployment\nDesign, build, and deploy end-to-end ML models including classification, regression, time-series, and NLP models.\n\nIntegrate ML models into production systems and applications.\n\nMonitor model performance and conduct analysis to detect data drift, quality issues, and opportunities for improvement.\n\nGenerative AI & RAG Application Development\nDevelop GenAI applications including RAG pipelines, prompt-engineered chatbots, and lightweight agentic workflows using frameworks such as LangChain, LlamaIndex, LangGraph, or Python.\n\nImplement effective RAG architectures including chunking strategies, embedding selection, vector store configuration (e.g., Qdrant, Milvus, pgvector), and retrieval evaluation.\n\nSoftware Engineering & Development Practices\nWrite clean, maintainable, and production-ready Python code.\n\nParticipate in code reviews and contribute to shared libraries and internal AI platform components.\n\nUse Git, CI/CD practices, and containerisation tools such as Docker in development workflows.\n\nCollaboration & Delivery\nWork closely with data engineers, MLOps engineers, and client stakeholders to move AI experiments into reliable production deployments.\n\nDocument technical approaches, solution designs, and experimental results clearly.\n\nResearch & Innovation\nStay up to date with the evolving AI landscape and introduce relevant tools, techniques, and best practices to the team.\n\n The ideal candidate should possess:\n2-5 years of hands-on experience in AI/ML engineering or data science roles\nDemonstrable track record of delivering at least 2-3 ML projects (beyond notebooks - production or near-production) and 1-2 GenAI / agentic AI projects\nStrong Python programming skills including Pandas, NumPy, Scikit-learn, and PyTorch or TensorFlow\nSolid understanding of machine learning fundamentals including feature engineering, model evaluation, bias-variance trade-off, and hyperparameter optimisation\nHands-on experience building RAG chatbots and applications using LLM APIs such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or equivalent\nWorking knowledge of RAG architecture decisions including chunking strategies, embedding model selection, vector store configuration, and retrieval quality evaluation\nPractical experience with at least one vector database such as Qdrant, Milvus, pgvector, or equivalent\nExperience with at least one cloud platform such as AWS, Azure, or GCP for model training or deployment\nProficiency with Git, CI/CD basics, and containerisation (Docker)\nAbility to read and write at least one additional programming language such as TypeScript/JavaScript, Java, Go, or advanced SQL\nPreferred Skills:\nExperience building voice AI bots using a pro-code approach such as Twilio, Amazon Connect, Deepgram, Whisper + TTS pipelines\nExposure to agentic patterns such as tool-calling agents, ReAct loops, multi-step planning, or reflection mechanisms \nFamiliarity with MLOps tooling such as MLflow, Weights & Biases (W&B), model registries, or orchestration tools like Airflow, Prefect, or ZenML \nExperience with evaluation frameworks for RAG and GenAI outputs such as RAGAS, DeepEval, or promptfoo \nKnowledge of responsible AI principles including bias detection, fairness metrics, and output guardrails \nExposure to NLP pipelines including entity extraction, text classification, and summarisation \nPrior experience in a systems integrator (SI), consultancy, or multi-client delivery environment\n Why Join NCS \nLead high-impact Data & AI advisory programs for major enterprises and public sector clients.\n\nShape enterprise strategies and governance frameworks that drive real transformation.\n\nWork with a talented, multidisciplinary team in a collaborative environment.\n\nCompetitive compensation and strong professional development support.\n\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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