AI-Native Engineer
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Key skills for this role
About the Role
We are seeking an AI-Native Software Engineer who views AI as a core collaborative partner in software delivery. You will architect systems, design specifications, and orchestrate multi-agent workflows.
Key Skills for This Role
Responsibilities
- Define high level system structures, API contracts, and data models before instructing AI tools to implement them
- Author rigorous, unambiguous technical specifications and context rules to guide AI agents toward deterministic, reviewable outputs
- Architect and own end to end Retrieval Augmented Generation pipelines, document ingestion, chunking strategy, embedding selection, vector store configuration, hybrid retrieval, and relevance evaluation
- Build and operate agent harnesses using orchestration frameworks (e.g. LangGraph, LangChain, AutoGen) including tool definitions, routing logic, guardrails, fallback paths, and evaluation hooks
- Design and enforce HITL gates for agentic write operations; know when to automate and when to require human sign off
- Review, test, and audit AI generated code for security vulnerabilities, performance characteristics, edge cases, and architectural alignment before it reaches production
Requirements
- Strong mastery of computer science fundamentals — data structures, algorithms, distributed systems, and system design
- Exceptional ability to read, evaluate, and critique AI generated code across multiple languages rapidly
- Hands on production experience building agent harnesses, multi agent orchestration pipelines, and supervisor/routing patterns using frameworks such as LangGraph, LangChain, or equivalent
- Practical experience designing RAG pipelines including vector store selection, embedding strategies, hybrid search, Reciprocal Rank Fusion, and retrieval quality evaluation
- 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)
- Proven ability to manage AI context windows, system instructions, tool schemas, and prompt structure to produce consistent, auditable outputs
- Solid experience with cloud native deployment (Azure, AWS, or GCP), RESTful API design, async patterns, and enterprise identity/auth integration
- Strong experience writing automated test suites to validate AI generated logic inside modern CI/CD pipelines, including adversarial and edge case coverage
Full Job Posting
Role Overview
- 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
- Define high level system structures, API contracts, and data models before instructing AI tools to implement them. Own the design, not just the execution.
- Author rigorous, unambiguous technical specifications and context rules to guide AI agents toward deterministic, reviewable outputs.
- Architect and own end to end Retrieval Augmented Generation pipelines, document ingestion, chunking strategy, embedding selection, vector store configuration, hybrid retrieval, and relevance evaluation.
- Build and operate agent harnesses using orchestration frameworks (e.g. LangGraph, LangChain, AutoGen) including tool definitions, routing logic, guardrails, fallback paths, and evaluation hooks.
- 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
- 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.
- Exceptional ability to read, evaluate, and critique AI generated code across multiple languages rapidly.
- Hands on production experience building agent harnesses, multi agent orchestration pipelines, and supervisor/routing patterns using frameworks such as LangGraph, LangChain, or equivalent.
- Practical experience designing RAG pipelines including vector store selection, embedding strategies, hybrid search, Reciprocal Rank Fusion, and retrieval quality evaluation.
- 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).
- Proven ability to manage AI context windows, system instructions, tool schemas, and prompt structure to produce consistent, auditable outputs.
- Solid experience with cloud native deployment (Azure, AWS, or GCP), RESTful API design, async patterns, and enterprise identity/auth integration.
- 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.
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