Unicorn Workforce
Website:
myunicorn.co.in
Company:
https://www.linkedin.com/company/unicorn-workforce
Industries: Software Development and IT System Custom Software Development
Job details:
Role: AI-Native Software Engineer
Years of Exp: 6-9 Years
Duration: 6 Months
Shift: General
Location: Remote
NOTE: Passport and UAN are mandatory and should be valid.
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.
You must possess deep foundational software engineering knowledge to evaluate, debug, and audit complex codebases generated by AI systems.
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.
Full-Stack Delivery: Contribute across backend services, APIs, and lightweight frontend surfaces as the scope demands. This is an IC role with end-to-end ownership.
Continuous Iteration: Guide AI agents through rapid prototyping loops, debugging cycles, and CI/CD pipelines with the same rigor applied to human-authored code.
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.
Behavioral Traits
Architect Mindset: Thrives on abstract problem-solving and systems thinking. Writes the spec before writing the prompt.
Rigorous Skepticism: Does not trust AI output blindly. Obsessively validates generated code against security vulnerabilities, edge cases, and architectural intent.
Spec-Driven Discipline: Aligns on explicit technical specifications before directing AI to generate, producing deterministic, reviewable results rather than aimless iteration.
Hyper-Efficient: Constantly identifies opportunities to automate development workflows and maximize output quality per unit of effort.
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.
Click on Apply to know more.