Role Overview
The Principal AI Architect is a senior technical leader responsible for the architectural integrity and strategic depth of NCS's most complex AI programmes. This is not a reference-architecture role — you go deep: design patterns, component-level trade-off analysis, cost modelling, and hands-on validation of the systems you design. You will engage with C-level and senior technical stakeholders on client side, shape multi-year AI platform roadmaps, and serve as the definitive technical authority across ML, GenAI, and agentic AI domains within NCS's AI practice.
Experience with architecting and building AI voice bot from scratch using livekit or similar framework would be a big plus.
Key Responsibilities
Architecture & Design
- Own end-to-end technical architecture for large-scale AI programmes: from data ingestion and model training through inference infrastructure, agentic orchestration, and front-end integration.
- Go beyond reference architecture: produce detailed design pattern specifications, component interaction diagrams, data flow models, and failure mode analyses.
- Make and defend technically grounded design decisions: LLM selection and cost modelling, build-vs-buy analysis, open-weight vs. proprietary model trade-offs, and infrastructure sizing.
- Design enterprise agentic AI systems incorporating multi-agent orchestration, RAG, AI memory, planning, reasoning, reflection, and tool integration via MCP or equivalent.
- Specify AI governance and safety architectures: guardrails, observability stacks, responsible AI controls aligned to NIST AI RMF, MAS TRM, and applicable regulatory frameworks.
- Define evaluation and quality frameworks across ML and GenAI: metrics selection, benchmark design, red-teaming protocols, and production monitoring strategies.
- Lead architecture reviews; challenge and validate designs proposed by AI Engineers and Senior AI Engineers.
Technical Depth & Hands-On Contribution
- Maintain active hands-on capability: prototype critical components, validate architectural assumptions through working code, and lead proof-of-concept builds.
- Deep working knowledge of the full AI stack: data pipelines, feature stores, model training (classical ML + fine-tuning LLMs), inference optimisation (quantisation, distillation, batching), and agentic orchestration.
- Evaluate and select from the evolving AI tooling ecosystem — vector databases, agentic frameworks, LLM gateways, MLOps platforms, observability tools — with defensible rationale.
- Design cost-optimised AI infrastructure: GPU/CPU sizing, quantisation strategies (GPTQ, AWQ, GGUF), cross-cloud connectivity, and FinOps for AI workloads.
Stakeholder Management & Delivery Leadership
- Serve as the senior technical authority in client engagements: lead architecture workshops, executive briefings, and technical governance reviews.
- Build trusted relationships with CTO, Head of AI, and senior engineering leaders on client side; navigate complex stakeholder dynamics across delivery programmes.
- Drive alignment between business objectives and technical architecture across multi-vendor, multi-workstream programmes.
- Provide architectural oversight and direction across delivery squads; escalate and resolve technical blockers at programme level.
- Contribute to presales: lead solution architecture for major bids, produce technically credible and commercially sound proposals.
- Develop and grow the AI engineering talent within the NCS AI practice; act as a technical mentor and role model across the team.
Thought Leadership & Practice Development
- Define and evolve NCS's internal AI architecture standards, design pattern libraries, and reusable platform components.
- Represent NCS externally: conference presentations, published thought leadership, and client advisory engagements.
- Identify emerging technology trends and translate them into practice capability investments.
Experience & Education
- 10–15+ years of progressive experience in AI/ML engineering, data science, or software engineering, with at least 5 years in architecture or technical leadership roles.
- Degree in Computer Science, Engineering, Statistics, or equivalent; advanced degree (MSc/PhD) advantageous.
- Proven track record designing and delivering large-scale AI programmes end-to-end — not just contributing to them.
- Experience building AI products or platforms from scratch, or serving as lead architect on enterprise AI programmes with significant complexity and scale.
- Background in system integration, technical consultancy, or professional services — with direct accountability for architecture quality in client-facing engagements.
Technical Depth
- Strong Python programming at production grade: modular, testable, well-documented code across ML and GenAI application stacks.
- Deep ML fundamentals: feature engineering, model evaluation, classical ML algorithms, bias-variance trade-offs, and hyperparameter optimisation.
- Extensive hands-on GenAI experience: RAG pipelines, LLM API integration (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI), prompt engineering, and evaluation.
- Expert-level knowledge of vector databases and embedding architectures (Qdrant, Milvus, pgvector) for semantic search and retrieval at scale.
- Deep multi-cloud platform expertise (AWS, Azure, GCP) across AI/ML services, model training, and inference infrastructure.
- Advanced containerisation and MLOps: Docker, Kubernetes, CI/CD pipelines for AI systems, model serving, and production monitoring.
- Multi-language programming proficiency; ability to read, reason about, and contribute to code across the full stack.
- Deep hands-on experience with agentic frameworks: LangGraph, CrewAI, AutoGen, OpenAI Agents SDK — at architectural depth, not just usage.
- Expert-level knowledge of agentic AI design patterns: multi-agent orchestration, memory architectures, planning and reasoning loops, reflection, and tool-use integration.
- Deep hands-on experience with GenAI infrastructure: LLM serving (vLLM, TGI, Ollama), fine-tuning (LoRA, QLoRA), model quantisation, and cross-cloud API architecture.
- Demonstrated ability to cost AI systems: TCO modelling, GPU/inference cost optimisation, build-vs-buy analysis across proprietary and open-weight LLMs.
- Strong command of responsible AI at architecture level: governance frameworks, safety controls, bias mitigation, auditability, and regulatory compliance.
Leadership & Stakeholder Skills
- Proven stakeholder management at senior levels: ability to advise, influence, and build confidence with client executives and technical leadership.
- Experience leading cross-functional, multi-vendor delivery programmes; comfortable operating in complex organisational environments.
- Strong written communication: ability to produce architecture documents, executive briefings, and technical proposals that are clear, precise, and persuasive.
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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