As a Senior AI Engineer at NCS, you will be a technical leader operating at the intersection of engineering excellence and client partnership. You will lead end-to-end delivery of AI workstreams, design and build advanced agentic AI solutions, and serve as the credible technical face of NCS AI capabilities in client engagements. Beyond your strong hands-on AI engineering capabilities, you will lead small technical teams, engage clients with confidence, translate business problems into sound AI architectures, and mentor junior engineers. You will own delivery workstreams, guide your squad, and drive the successful implementation of next-generation AI-powered solutions across government, telco, financial services, and e-commerce sectors.
What Will You Do?
Technical Delivery & AI Solution Development
- Lead end-to-end delivery of AI workstreams from requirements through data preparation, modelling, integration, testing, and production handover
- Design and build advanced agentic AI solutions including multi-agent systems, RAG pipelines with AI memory, planning and reasoning loops, and reflection patterns
- Architect GenAI application stacks including LLM orchestration layers, observability tools (Langfuse, Braintrust), guardrails, and LLM gateway patterns (LiteLLM, Portkey)
- Develop production-grade ML systems alongside GenAI components, maintaining quality across both paradigms
- Design, build, and deploy end-to-end ML models (classification, regression, time-series, NLP) and integrate them into production systems
- Develop GenAI applications including RAG pipelines, prompt-engineered chatbots, and lightweight agentic workflows using frameworks such as LangChain, LlamaIndex, LangGraph, or plain Python
- Implement effective RAG architectures including chunking strategies, embedding selection, vector store configuration (Qdrant, Milvus, pgvector), and retrieval evaluation
- Build and maintain evaluation harnesses for AI system quality — correctness, faithfulness, latency, and safety
Engineering Excellence & Quality Assurance
- Conduct technical code reviews, enforce engineering standards, and introduce tooling that improves team velocity
- Write clean, maintainable Python code; participate in code reviews; contribute to shared libraries and internal AI platform components
- Design multi-component AI architectures and communicate them via clear diagrams and written specifications
Client & Stakeholder Engagement
- Serve as a primary technical point of contact for client teams in day-to-day delivery interactions
- Lead or co-facilitate client workshops including requirements discovery, solution walkthroughs, sprint demos, and retrospectives
- Translate ambiguous client requirements into well-defined technical user stories and acceptance criteria
- Communicate technical trade-offs and design decisions in clear, accessible language appropriate for business stakeholders
- Manage client expectations proactively, surface risks early, and propose mitigations
Team Leadership & Mentorship
- Technically lead a small squad of 2–4 AI engineers; delegate tasks, review outputs, and coach on best practices
- Mentor junior AI engineers on both technical skills and professional conduct in client settings
- Contribute to internal knowledge-sharing through technical guidelines, lunch-and-learns, and building reusable AI platform components
The Ideal Candidate Should Possess:
Required Experience & Background
- 5–8 years of hands-on AI/ML engineering or data science experience, with at least 2–3 years in client-facing or technical-lead roles
- Proven delivery of complex AI projects end-to-end in a professional services, consulting, or systems integration environment
- Demonstrable agentic AI project experience with systems incorporating RAG, AI memory, reflection, planning, and/or reasoning patterns
Core Technical Competencies
- Strong Python programming with Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow; comfortable writing modular, testable code
- Solid understanding of ML fundamentals including feature engineering, model evaluation, bias-variance trade-off, and hyperparameter optimisation
- Hands-on GenAI experience building RAG chatbots and applications, working with LLM APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or equivalent)
- Familiarity with vector databases and embedding models for semantic search and retrieval (e.g., Qdrant, Milvus, pgvector)
- Experience with at least one cloud platform (AWS, Azure, or GCP) for model training or deployment
- Proficiency with Git, CI/CD, and containerisation (Docker); familiarity with MLOps practices
- Proficiency in at least one additional programming language beyond Python (e.g., TypeScript/JavaScript, Java, Go, or advanced SQL)
- Deep hands-on experience with agentic frameworks such as LangGraph, CrewAI, AutoGen, or OpenAI Agents SDK
- Experience with LLM observability, evaluation, and guardrails tooling in production environments
- Comfort with multiple programming languages and full-stack awareness (APIs, front-end integration, data pipelines)
- Working knowledge of MLOps and AI infrastructure including model serving, monitoring, and CI/CD for AI systems
Client Engagement & Communication Skills
- Proven experience engaging directly with client stakeholders — including non-technical business owners — on AI solution design and delivery
- Strong written and verbal communication skills; able to produce client-ready documentation, presentations, and technical specifications
- Experience managing small delivery teams and coordinating across cross-functional groups
Preferred Qualifications (Nice-to-Have)
- Experience building voice AI bots using a pro-code approach (Amazon Connect, Twilio, Deepgram, Whisper / ElevenLabs + custom orchestration, or LiveKit) — a strong differentiator for this role
- Experience with MCP (Model Context Protocol) for tool integration in enterprise agentic systems
- Familiarity with responsible AI frameworks such as NIST AI RMF, MAS TRM, or equivalent governance standards
- Exposure to red-teaming and AI safety testing (OWASP LLM Top 10, promptfoo, adversarial prompt evaluation)
- Presales support experience including contributing to proposals, RFP responses, or solution scoping
- Domain experience in government, financial services, telco, or e-commerce AI use cases
- AWS, Azure, or GCP certifications (ML or AI specialty) are preferred but not mandatory
Education Qualification
- Degree in Statistics, Computer Science, Engineering, Data Science, or equivalent quantitative discipline
Why Join NCS
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.
Scam Alert
We 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.
Please 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. When in doubt, verify the sender’s identity by contacting us at [email protected].