Job Description :

Full Stack AI Engineer

Role/Responsibilities :

- Author precise feature specs covering requirements, edge cases, constraints, and acceptance criteria before any code is written or AI agent is invoked.

- Own features end-to-end translating user outcomes into acceptance criteria, driving development through AI-assisted workflows, and validating output against the spec before delivery.

- Design and run generator-critic pipelines using AI to generate output and a structured critique pass against the same spec to validate it, iterating until acceptance criteria are met.

- Build and maintain software components APIs, data layers, services, and integrations as the engineering foundation that AI-native workflows operate on top of.

- Evaluate and adopt AI tools with structured reasoning assess new tooling against defined criteria, bring recommendations with evidence, and stay current with the evolving AI development landscape.

- Work autonomously across development, quality, and requirements minimising handoffs through disciplined spec authorship, self-managed validation, and proactive flagging of gaps before building.

Required Skills and Experience :

- Bachelor's degree in software engineering, Computer Science, or a related technical field.

- 5+ years of overall software development experience, with at least 3 years actively using AI tools as a core part of the development workflow.

- Proficiency in .NET, MS SQL Server (or equivalent RDBMS), MongoDB (or equivalent NoSQL), and API development.

- Ability to write structured, machine-precise specs that serve as ground truth for both code generation and output validation.

- Practical understanding of prompt construction, context engineering, and diagnosing AI output failures.

- Working awareness of the AI tool ecosystem intelligent system tooling (LLM APIs, RAG, vector databases, agent frameworks) and SDLC acceleration tooling (agentic coding, AI code review, AI test generation).

- Strong written communication skills and experience working in Agile or Kanban environments with end-to-end ownership of deliverables.

Nice to Have Qualities & Skills :

- Hands-on experience building RAG pipelines, working with vector databases, or integrating LLM APIs into production systems.

- Experience with multi-agent orchestration frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.

- Familiarity with AI observability tooling such as LangSmith, LangFuse, or Braintrust.

- Exposure to spec-driven development practices such as BMAD or GitHub Spec Kit.

- Exposure to real estate technology, mortgage, or related financial services domain.

- Awareness of Model Context Protocol (MCP) and its role in connecting AI agents to external tools and data sources.