AI Native Engineer

Job Summary

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.