AI Native Engineer

Job Summary

We are looking for an AI-Native Software Engineer who treats AI as an essential collaborator in software creation rather than merely an autocomplete tool. This role focuses on reducing manual coding of boilerplate and emphasizes system architecture, precise technical specification writing, and managing multi-agent workflows.

Core Responsibilities

• Design and define comprehensive system architectures, including API contracts and data models, guiding AI tools to execute these designs effectively. Take full ownership of design aspects, not just code execution.
• Create clear, unambiguous technical specifications and context rules that steer AI agents to generate deterministic and easily reviewable outputs.
• Develop and maintain end-to-end Retrieval-Augmented Generation (RAG) pipelines, handling document ingestion, chunking strategy, embedding method selection, vector store setup, hybrid retrieval, and relevance assessments.
• Construct and manage agentic workflows using orchestration frameworks like LangGraph, LangChain, or AutoGen. This includes defining tools, routing mechanisms, guardrails, fallback options, and evaluation hooks.
• Implement Human-in-the-Loop (HITL) checkpoints for agentic write operations to balance automation with required human approvals, especially for irreversible or critical actions.
• Conduct thorough review, testing, and auditing of AI-generated code to identify security risks, assess performance, cover edge cases, and ensure alignment with architectural standards before deployment.

Required Technical Skills

• Strong knowledge of computer science fundamentals including data structures, algorithms, distributed systems, and system design to effectively detect and correct AI-generated errors.
• Expertise in evaluating and critically reviewing AI-produced code across various programming languages swiftly.
• Direct experience in building production-ready agent harnesses and multi-agent orchestration pipelines, applying supervisor and routing patterns using frameworks such as LangGraph or LangChain.
• Proven experience designing and implementing RAG pipelines encompassing vector store choices, embedding strategies, hybrid search methods, Reciprocal Rank Fusion, and evaluation of retrieval quality.
• Hands-on proficiency with AI-native development environments like Cursor, Windsurf, GitHub Copilot, and command-line agentic tools including Claude Code, Aider, or Codex CLI.
• Skill in managing AI context windows, system instructions, tool schemas, and structuring prompts for consistent, traceable outputs.
• Experience with cloud-native deployments on platforms such as Azure, AWS, or GCP, including RESTful API design, asynchronous programming patterns, and enterprise identity and authentication integration.
• Strong background in developing automated test suites for validating AI-generated logic within modern CI/CD pipelines, including adversarial testing and edge case handling.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or equivalent hands-on production experience.
• Experience integrating agentic or API-oriented solutions with enterprise HR, workforce, or ERP systems like SAP SuccessFactors, Workday, Concur, or Oracle HCM.
• Practical machine learning expertise beyond using APIs, such as model fine-tuning, training pipelines, evaluation frameworks, or MLOps deployments.
• Familiarity with enterprise identity providers like OKTA or Azure AD and secure token management in agentic environments.
• A portfolio or GitHub showcasing projects developed primarily using agentic or specification-driven development approaches.