Responsibilities: Build and ship AI agents and automation harnesses as a core deliverable - not a side experiment - using tool use/function calling, multi-turn context management, and agentic design patterns (MCP, LangChain-style frameworks). Take a problem from rough idea to deployed, working software with minimal handoffs - writing code, shaping UX, and wiring data pipelines yourself, accelerated by AI tooling throughout.
Numbers & Facts
Location
Austin, TX
Description
Our Client, an IT Services and Consultant company, is looking for a Senior AI Solutions Architect for their Austin, TX /Bloomfield, CT/ Hybrid location.
Responsibilities:
Build and ship AI agents and automation harnesses as a core deliverable - not a side experiment - using tool use/function calling, multi-turn context management, and agentic design patterns (MCP, LangChain-style frameworks)
Use Claude, Cursor, and Codex as your primary development environment daily - build with AI, not around it, across every layer you touch
Evaluate and correct non-deterministic model output as a first-class engineering discipline - know what the AI wrote, where you overrode or discarded it, and what would have shipped broken if trusted blindly
Take a problem from rough idea to deployed, working software with minimal handoffs - writing code, shaping UX, and wiring data pipelines yourself, accelerated by AI tooling throughout
Design agent skills and internal AI-assisted workflows that other engineers on the team rely on and build from
Move across frontend, backend, data engineering, and infra within the same sprint, using AI tools to compress the time each layer normally takes
Design and maintain data pipelines and analytical surfaces on Databricks and AWS that non-engineers can actually use
Work directly with product managers and stakeholders - push back on scope, propose better (often AI-driven) solutions, and make pragmatic trade-offs without waiting to be told
Own architectural decisions for your product area, including when an agent/LLM-based approach is the right call versus deterministic code
Leave the codebase simpler than you found it - know when to abstract, inline, or simplify rather than add
Deploy, debug, and operate confidently in AWS without breaking production
Deliver outcomes that would take a conventional team 5-10x longer - the agentic/AI-native workflow itself is the reason for that multiplier, not just raw coding speed