Principal AI Architect

Company Overview

Trianz is an applied AI solutions company that accelerates customer business transformation through AI powered Transformation Services as a Software Model. With 25+ years of transforming enterprises, we've evolved to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and SecOps - delivered through strategic partnerships with leading hyperscalers.

We're building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence - RevolutionAIzing Transformations.

Role Overview

Trianz is scaling its private and sovereign AI capabilities to meet enterprise demand for on-premises, air-gapped, and provider-agnostic LLM deployment. We're seeking a Principal AI Architect to own the complete technical architecture for deploying large language models inside customer environments — not consuming cloud APIs but designing the systems that run LLMs with full sovereignty and control.

This is a pure individual contributor role with architectural authority. You will define how models are selected, served, routed, monitored, and governed across multi-cloud, on-premises, and hybrid environments. You'll work with enterprise customers running on AWS, Azure, GCP, RedHat OpenShift, VMware, and air-gapped infrastructure.

This role demands deep hands-on expertise in production LLM serving, GPU/CPU optimization, Kubernetes at scale, and the ability to make architecture decisions in complex, regulated environments. You'll be the authority that production AI engineers look to for guidance.

Key Responsibilities

LLM Serving Architecture

Design the complete private LLM serving stack: model selection, serving frameworks (vLLM, TensorRT-LLM, Triton), and production topology

Define intelligent CPU vs GPU routing policies based on latency, cost, throughput, and model characteristics

Architect model serving for diverse hardware: NVIDIA GPUs, Intel Xeon CPUs, AMD EPYC, and heterogeneous clusters

Evaluate open-source models (Llama, Mistral, Qwen, Phi) against closed models for enterprise use cases

Multi-Cloud & On-Premises Deployment

Architect multi-cloud model serving (AWS, Azure, GCP) with provider-agnostic, no-lock-in design

Design on-premises serving for enterprise customers: RedHat OpenShift, VMware, containerized model serving on customer hardware

Architect air-gapped, sovereign deployment patterns with data-residency compliance

Design per-tenant model isolation and security boundaries

Model Lifecycle & Operations

Define release architecture for model versions: staged rollout, deployment gates, rollback, and promotion strategies

Design the LLM governance framework: model behavior monitoring, inference audit logging, guardrail architecture

Architect DevSecOps pipeline and self-service deployment automation standards

Design model quantization strategies (GPTQ, AWQ, GGUF) and routing trade-offs for cost and performance

Architecture Authority & Governance

Produce architecture sign-off documents and review all AI system designs before implementation

Define standards for production LLM deployment, security, compliance, and scalability

Establish best practices for GPU FinOps and cost modeling across mixed inference fleets

Lead technical decisions on inference schedulers and custom optimization approaches

Ideal Candidate Profile

Experience: 15 + years

Production LLM Deployment:

Deployed LLMs to production in a real enterprise environment — not just API consumption

Hands-on with vLLM, TensorRT-LLM, or Triton in a production serving context

Designed CPU cluster inference (Intel Xeon / AMD EPYC) for open-source models

8+ years in AI/software architecture with at least 3 years in production LLM systems

Infrastructure & Scale:

Kubernetes at production scale (EKS, AKS, GKE, or OpenShift) — not just local k8s

Designed multi-cloud, provider-agnostic AI architectures

Experience with model quantization (GPTQ, AWQ, GGUF) and routing trade-offs

Technical Depth

Deep understanding of LLM serving frameworks and inference optimization

Experience with GPU memory management, batching, and throughput optimization

Understanding of model serving topologies: single-model, ensemble, and traffic-driven routing

Proficiency in containerization and orchestration at enterprise scale

Knowledge of security, compliance, and data residency in regulated environments

Mindset & Fit

Architect-level thinking: ability to make decisions with incomplete information and trade-offs

Comfort with ambiguity in emerging sovereign AI landscape

Driven by technical depth and system design excellence, not titles

Collaborative: works across teams to translate business requirements into architecture

Bias toward open-source, extensible solutions over proprietary managed services

Nice to Have

RedHat OpenShift for on-premises model serving

NVIDIA Dynamo, SGLang, or custom inference schedulers

GPU FinOps and cost modeling for mixed CPU/GPU inference fleets

Sovereign AI or regulated-industry deployment experience (healthcare, finance, defense)

Intel OpenVINO or AMD ROCm for CPU-optimized inference

Experience with model monitoring, observability, and telemetry at scale

Previous role at hyperscalers (AWS, Azure, GCP) in the AI/ML infrastructure space

Background in edge computing, IoT, or on-premises AI systems

Why Join Trianz

Architectural Impact: Own the complete private AI infrastructure for Fortune 500 enterprises. Your decisions influence how LLMs run in the most security-conscious, regulated environments globally.

Technical Excellence: Work with cutting-edge inference frameworks, open-source models, and heterogeneous hardware. Design systems that work across AWS, Azure, GCP, on-prem, and air-gapped environments — not just API consumption.

Sovereign AI Leadership: Lead the charge in sovereign AI deployment. Help enterprises reclaim control of their AI infrastructure while maintaining the flexibility to scale globally.

Zero Bureaucracy: Pure IC role with architectural authority. No slow approval cycles — your design decisions move fast into production.

Enterprise Scale: Work on transformations across Fortune 500 organizations. See your architecture deployed across continents, industries, and mission-critical use cases.

Growth Through Ambiguity: Thrive in the emerging sovereign AI space where problems are complex, standards are evolving, and your expertise will define the industry.