AI Technical Architect
First seen by Alion on Sep 3, 2026.
Responsibilities:
Technical Discovery & Solution Design (40%):
- Lead discovery workshops: Run technical discovery sessions with client stakeholders to understand business processes, pain points, data landscapes, and transformation goals.
- Architect AI-native solutions: Design end-to-end system architectures - covering agentic AI components (multi-agent workflows, RAG, LLM orchestration), application layers (Next.js, TypeScript, Medusa), data infrastructure (PostgreSQL, vector DBs), and cloud deployment across our partner platforms (AWS Bedrock, Gemini Enterprise / Vertex AI on Google Cloud).
- Evaluate and recommend: Assess build-vs-buy decisions, technology stack choices, model selection, and integration approaches based on client constraints and ROI.
- Create technical proposals: Produce detailed architecture documents, system diagrams, and technical narratives that win client trust and close deals.
Engineering Estimation & Planning (30%):
- Build accurate estimates: Break down complex engagements into phased delivery plans with task-level estimates across modules, roles, and timelines.
- Define team composition: Specify the right mix of AI Engineers, AI-Native Engineers, and Platform Engineers for each engagement; factor in AI-assisted productivity gains.
- Risk assessment: Identify technical risks, dependency chains, and integration complexity; build mitigation strategies into project plans.
- Scope management: Work with Practice Leaders and Outcome Managers to balance ambition with delivery reality - protecting both client outcomes and team sustainability.
Technical Leadership & Pre-Sales (30%):
- Technical credibility in sales: Join pre-sales conversations to provide technical depth, answer architecture questions, and demonstrate devx labs' engineering capability.
- POC and prototype leadership: Build rapid proofs-of-concept that validate technical feasibility and demonstrate value before full engagement commitment.
- Cross-practice knowledge: Maintain deep understanding across all three practice areas - Customer Interactions, AI-Led Business Operations, and Enterprise Architecture - to architect integrated solutions.
- Mentor and elevate: Guide Senior AI Engineers on architectural thinking, system design principles, and client communication skills.
Skills
Artificial Intelligence, Generative AI, Microservices Architecture, AI Architecture, RAG, LLM