Job Summary

We are seeking an AI-Native Software Engineer who views AI not just as an autocomplete tool, but as a core collaborative partner in software delivery. In this role, you will spend less time manually writing boilerplate and more time architecting systems, designing precise technical specifications, and orchestrating multi-agent workflows.

Core Responsibilities

System Architecture & Design: Define high-level system structures, API contracts, and data models before instructing AI tools to implement them. Own the design, not just the execution.
Context Engineering & Spec Writing: Author rigorous, unambiguous technical specifications and context rules to guide AI agents toward deterministic, reviewable outputs.
RAG Pipeline Design: Architect and own end-to-end Retrieval-Augmented Generation pipelines, document ingestion, chunking strategy, embedding selection, vector store configuration, hybrid retrieval, and relevance evaluation.
Agentic Workflow Management: Build and operate agent harnesses using orchestration frameworks (e.g. LangGraph, LangChain, AutoGen) including tool definitions, routing logic, guardrails, fallback paths, and evaluation hooks.
Human-in-the-Loop Validation: Design and enforce HITL gates for agentic write operations. Know when to automate and when to require human sign-off, especially for irreversible or high-stakes actions.
Review, test, and audit AI-generated code for security vulnerabilities, performance characteristics, edge cases, and architectural alignment before it reaches production.

Required Technical Skills

Engineering Fundamentals: Strong mastery of computer science fundamentals — data structures, algorithms, distributed systems, and system design. You must be able to catch and correct AI errors because you understand the underlying systems.
Code Review & Auditing: Exceptional ability to read, evaluate, and critique AI-generated code across multiple languages rapidly.
Agentic System Design: Hands-on production experience building agent harnesses, multi-agent orchestration pipelines, and supervisor/routing patterns using frameworks such as LangGraph, LangChain, or equivalent.
RAG & Retrieval Engineering: Practical experience designing RAG pipelines including vector store selection, embedding strategies, hybrid search, Reciprocal Rank Fusion, and retrieval quality evaluation.
AI Tooling Proficiency: Advanced hands-on experience with AI-native IDEs (e.g. Cursor, Windsurf, GitHub Copilot) and command-line agentic tools (e.g. Claude Code, Aider, Codex CLI).
Context & Prompt Engineering: Proven ability to manage AI context windows, system instructions, tool schemas, and prompt structure to produce consistent, auditable outputs.
Cloud & API Integration: Solid experience with cloud-native deployment (Azure, AWS, or GCP), RESTful API design, async patterns, and enterprise identity/auth integration.
Testing & CI/CD: Strong experience writing automated test suites to validate AI-generated logic inside modern CI/CD pipelines, including adversarial and edge-case coverage.

Preferred Qualifications

Bachelor's or Master's degree in Computer Science, Software Engineering, or equivalent deep production experience.
Experience integrating with enterprise HR, workforce, or ERP platforms (e.g. SAP SuccessFactors, Workday, Concur, or Oracle HCM) — particularly in an agentic or API integration context.
Hands-on ML experience beyond API consumption: model fine-tuning, training pipelines, evaluation frameworks, or MLOps deployment.
Familiarity with enterprise identity providers (e.g. OKTA, Azure AD) and secure token handling in agentic contexts.
A portfolio or GitHub repository demonstrating projects built primarily via agentic or spec-driven development methodologies.

AI Native Engineer