Principal AI Engineer

This is a principal-level engineering role for a strong engineer and architect who has made AI-native delivery their default way of working. You bring deep software engineering and architecture experience, and you deliver through an AI-native SDLC: you define the work and the quality bar (architecture, specifications, acceptance criteria), use AI agents to do the heavy lifting, and remain accountable for what ships.

This is an Engineer role, not an advisory one. You deliver. You drive projects hands‑on, writing and shipping the work yourself (now amplified by agents), not directing it from a distance. Principal here means depth and ownership at the keyboard, not a step away from it. You will be one of the engineers who defines how Altex Soft delivers software, internally and on client projects, at substantially higher velocity without sacrificing engineering quality.

Stack‑agnostic: we care that you are an excellent engineer who works AI‑first by default.

• Deep software engineering and architecture experience (system design, testing, refactoring, code review) at a level where you confidently judge and correct anything an AI produces.
• Demonstrable AI-first delivery experience: you already ship real, production work primarily through agentic tools. Be ready to show how.
• Fluency with modern AI coding environments (Claude Code, Cursor, or equivalent), including plan and agent modes.
• Specification-driven, context-engineering discipline: you frame the problem and the design before you let an agent run.
• Sound judgment on when not to trust the agent: you catch hallucinated APIs, silent logic errors, and LLM antipatterns in generated code.
• Autonomous — take a vague goal, scope it, and drive it to delivery without waiting to be told the next step.
• Business-oriented and business-driven — optimize for client and business outcomes, not technical elegance for its own sake.
• Ownership — accountable for outcomes end-to-end, including what an agent produced under your direction.
• Decisiveness under ambiguity — sound calls with incomplete information, adjusted as reality lands.
• Strong communication — explain technical decisions to clients and non-engineers; make the implicit explicit for teammates and agents.
• Pragmatism over dogma — pick the approach that ships quality fastest, not the most fashionable one.
• Mentorship — raise the people around you, not just your own output.

• Hands‑on experience with spec‑driven frameworks (BMAD, Open Spec, Superpowers).
• Brownfield AI transformation: bringing agentic delivery into a legacy codebase.
• Building graph and MCP context layers so agents can pull real project context.
• Experience designing and shipping AI‑powered product capabilities (LLM, RAG, agents).
• Token-cost and Fin Ops thinking for agentic engineering.

Principal-level, hands‑on engineering role: you deliver through an AI-native SDLC — define the work and quality bar (architecture, specifications, acceptance criteria), use AI agents for the heavy lifting, and remain accountable for what ships. This is an Engineer role, not advisory: you write and ship the work yourself, amplified by agents. Stack‑agnostic.

• Own end‑to‑end delivery of complex features and services through an agentic, specification‑driven workflow, with architecture and acceptance criteria defined up front and AI executing the build under your direction.
• Architect systems that are legible to both humans and agents, and make the design and tooling calls that let a team deliver AI‑first.
• Orchestrate AI coding agents (Claude Code, Cursor agent layer, Kiro and similar) across targeted edits and large orchestrated refactors and builds; selecting the right tool for the job is part of the craft.
• Practice agentic TDD: tests and…