# Insight Global — Technical Architect (AI)

- Generated: 2026-09-10 06:28:16 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4463892281/
- Provider: LinkedIn
- Posted: approximately 2026-09-09 12:28 PM EDT (from '18 hours ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp
- Applicants: 26 applicants
- Work model/location: United States; work model not disclosed
- Compensation: Not disclosed
- Travel: Not disclosed
- Positioning track: Technical IC
- Fit outcome: FAIL — 78%

## Direct-match strengths

Production agentic AI, RAG, LangChain/LangGraph, MCP, evaluation, observability, governance, AWS, distributed systems, Kubernetes, databases, client architecture, reusable accelerators, and regulated-industry experience.

## Hard or material gaps

Hard compensation-policy failure for a non-Director/VP/Chief title, and the work model is unverifiable. Material technical gaps include required architecture across at least two major clouds and deep neural-network fundamentals spanning CNNs/RNNs; Keith's strongest direct depth is AWS and applied production GenAI.

## Evidence map

1. Production-grade agentic AI (weight 3, evidence 3/3) — Direct AssistX production implementation.
2. System design and AWS distributed architecture (weight 3, evidence 3/3) — Direct cloud-native, Kubernetes, database, security, and reliability work.
3. Two-major-cloud depth (weight 3, evidence 1/3) — AWS is deep; Google Vertex exposure does not establish broad GCP architecture and Azure is not claimed.
4. Traditional ML internals across transformers/CNNs/RNNs (weight 3, evidence 1/3) — ML and TensorFlow are direct; the requested breadth/depth is not fully documented.
5. RAG, embeddings, evaluation, observability, MCP (weight 3, evidence 3/3) — Direct and recent.
6. Client architecture and reusable IP (weight 2, evidence 3/3) — Direct AWS/NorthBay consulting and accelerator development.
7. Compensation and work model (weight 3, evidence 0/3) — Both fail Keith's constraints.

## Full normalized job description

Technical Architect
Program Delivery & IP Engineering
The Role
We are looking for a Technical Architect who operates at the seam between client-facing delivery and internal IP development. You will partner directly with enterprise clients to define AI and data architecture, and then translate the hard-won patterns, decisions, and frameworks from those engagements into reusable assets that IG Labs owns and re-deploys.
This is not a pure consulting role and it is not a pure product role. It is the connector between them. You will be embedded in active engagements, you will shape architecture under real constraints, and you will be accountable for making sure what we build for one client becomes leverage for the next.
The Technical Baseline: Three Pillars
The Technical Architect is evaluated against the same three pillars as our FDEs, at greater depth and breadth. You are the person who sets the technical bar, so you have to clear it convincingly in all three. Interviews probe each pillar directly.
What we evaluate, and what “deep” looks like
1. System Design
For the Technical Architect, system design is broad and deep. Everything expected of an FDE (databases, data modeling, strong programming) plus the architect's canvas: cloud architecture across at least two major providers, networking and connectivity, containerization and Kubernetes/orchestration, distributed systems and scalability, reliability and observability, and security and compliance as first-class design constraints. You reason about trade-offs across the whole system, not just one workstream.
2. Traditional AI / ML
A real understanding of how models actually work, not just how to call them. Neural network fundamentals across transformers, CNNs, and RNNs — attention, tokenization, training vs. inference, loss and evaluation, overfitting and regularization. Above all, a deep working understanding of embedding spaces: how text and other modalities become vectors, what distance and similarity mean, dimensionality, and how embeddings drive retrieval, clustering, and semantic matching.
3. Applied AI / Agentic AI
Very deep, hands-on experience building production-grade agentic systems. Not demos. Orchestration and control flow, tool and function calling, RAG and context engineering, memory and state, multi-step planning, evaluation and guardrails, cost and latency management, observability, and safe deployment into real environments. You have shipped agents that real users depend on, and you know why the hard ones fail.
The difference from the FDE bar is scope: an FDE goes deep on a workstream, the Architect reasons across the whole system and across concurrent engagements.
Key Responsibilities
Client Architecture Definition
Partner with client stakeholders (CTO, VP Engineering, Chief Data Officers) to define AI and data platform architecture aligned to their strategic objectives
Lead architecture discovery sessions: current-state assessment, gap analysis, future-state design, and roadmap sequencing
Produce and own architectural artifacts: reference architectures, decision records (ADRs), data flow diagrams, integration blueprints, and governance frameworks
Define and enforce non-functional requirements across security, scalability, observability, and regulatory compliance
Serve as the technical authority on engagements, bridging business intent and engineering execution
IG Labs Collaboration & IP Derivation
Work hand-in-hand with the IG Labs team to identify reuse opportunities within active client engagements
Abstract generalizable patterns from bespoke client solutions (reference architectures, design templates, evaluation frameworks, deployment playbooks) and contribute them to the IP library
Participate in IP review cycles: peer review of contributed assets, validation of generalizability, and documentation of applicability conditions
Actively drive toward Reuse Index targets, the primary delivery health metric tracking what proportion of each engagement is powered by pre-built IP
Identify net-new IP opportunities from emerging client problems and partner with Labs to prototype and productize
Program Delivery
Provide architectural oversight and technical governance across multiple concurrent client engagements
Partner with delivery leads and project managers to ensure architectural decisions are reflected in project plans, sprint goals, and acceptance criteria
Support pre-sales and solution design: contribute to SOWs, RFP responses, and technical proposals with defensible architectural thinking
Mentor delivery engineers and junior architects, elevating technical craft across the team
Flag architectural drift, scope risk, and technical debt in active engagements before they become delivery blockers
Qualifications
Required:
8+ years in technical architecture, solutions architecture, or principal engineering roles
System design breadth and depth: cloud-native architecture across at least two major platforms (AWS, Azure, GCP), networking, containerization and Kubernetes, distributed systems, plus strong databases and data modeling
Traditional AI/ML understanding: how transformers, CNNs, and RNNs work, and a deep working grasp of embedding spaces
Hands-on experience designing and shipping production-grade agentic AI systems, not just prototypes
Experience working directly with senior client stakeholders to define architecture
Strong written and visual communication: architecture diagrams, decision records, executive summaries
Demonstrated ability to abstract reusable patterns from specific implementations
Comfortable operating with ambiguity in professional services or consulting environments
Strongly Preferred:
Deep experience with RAG architectures, LLM orchestration (LangChain, LlamaIndex, or similar), and agent evaluation/observability
Familiarity with MCP (Model Context Protocol) and its role in AI system integration
MLOps platform design, model governance, and AI lifecycle management
Experience with embedding/vector stores and retrieval design at enterprise scale
Background in regulated industries: financial services, healthcare, or insurance
Prior experience in a professional services, systems integrator, or technology consulting firm
Track record of contributing to an IP library, accelerator repository, or internal knowledge base
Executive Certificate, Master’s, or equivalent advanced credentials
Core Competencies
Systems Thinking
Client Partnership
IP Mindset
Technical Authority
Pattern Recognition
Architectural Communication
Governance
Delivery Accountability

## Artifact metadata

- Resume: Not generated because the fit gate returned FAIL.
- Cover letter: Not generated because the fit gate returned FAIL.
- LinkedIn note: Not generated for a FAIL role.
- Google Drive used: No