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.
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Core Responsibilities:
- System Architecture & Design: Define high-level system structures, API contracts, and data models before instructing AI tools to implement them.
- 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, 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.
- Review and Audit: Review, test, and audit AI-generated code for vulnerabilities, performance, edge cases, and architectural alignment before production.
Required Technical Skills:
- Strong mastery of computer science fundamentals, including data structures, algorithms, distributed systems, and system design.
- Exceptional ability to read, evaluate, and critique AI-generated code across multiple languages.
- Hands-on production experience with agent harnesses and multi-agent orchestration pipelines using frameworks such as LangGraph or LangChain.
- Practical experience in designing RAG pipelines and working with AI-native IDEs and command-line tools.
- Solid experience in cloud-native deployment, RESTful API design, and enterprise identity/auth integration.
- Experience in writing automated test suites to validate AI-generated logic within CI/CD pipelines.
Preferred Qualifications:
- Bachelor's or Master's degree in Computer Science, Software Engineering, or equivalent experience.
- Knowledge of enterprise HR or ERP platforms integration.
- Hands-on experience with model fine-tuning, training pipelines, and evaluation frameworks.
- Familiarity with enterprise identity providers and secure token handling.
- A portfolio or GitHub repository demonstrating projects built via agentic or spec-driven development methodologies.
