Full Stack AI Engineer

JobItUs; Ahmedabad District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

JobItUs in Ahmedabad is seeking an experienced software engineer to author precise feature specs, own end-to-end delivery, and drive AI-assisted development workflows. You will design generator-critic pipelines, build APIs and data layers, and evaluate AI tools while maintaining strong Agile collaboration and proactive quality controls.

The role requires a Bachelor’s degree in software engineering or CS, 5+ years of software development with 3+ years using AI tools, and proficiency in .NET, MS

Qualifications

• Bachelor's degree in Software Engineering, Computer Science, or a related technical field.
• 5+ years of software development experience with at least 3 years using AI tools as part of the workflow.
• Proficiency in .NET, MS SQL Server (or equivalent RDBMS), MongoDB (or NoSQL), and API development.
• Ability to write structured, machine-precise specs for code generation and validation.
• Knowledge of prompt construction, context engineering, and diagnosing AI output failures.

Responsibilities

• Author precise feature specs covering requirements, edge cases, constraints, and acceptance criteria.
• Own features end-to-end — translating outcomes into acceptance criteria and validating output against the spec.
• Design and run generator-critic pipelines to generate output and critique passes against the spec.
• Build and maintain software components — APIs, data layers, services, integrations for AI workflows.
• Evaluate AI tools with structured reasoning and stay current with evolving AI development tech.
• Work autonomously across development, quality, and requirements with proactive gap flagging.

Skills

Structured specs

Agile / Kanban

AI tooling

Prompt construction

Communication

Education

Bachelor's degree in Software Eng./CS

Tools

.NET

MS SQL Server

MongoDB

API development

Job description

Role/Responsibilities

Author precise feature specs covering requirements, edge cases, constraints, and acceptance criteria before any code is written or AI agent is invoked.

Own features end-to-end — translating user outcomes into acceptance criteria, driving development through AI-assisted workflows, and validating output against the spec before delivery.

Design and run generator-critic pipelines — using AI to generate output and a structured critique pass against the same spec to validate it, iterating until acceptance criteria are met.

Build and maintain software components — APIs, data layers, services, and integrations — as the engineering foundation that AI-native workflows operate on top of.

Evaluate and adopt AI tools with structured reasoning — assess new tooling against defined criteria, bring recommendations with evidence, and stay current with the evolving AI development landscape.

Work autonomously across development, quality, and requirements — minimising handoffs through disciplined spec authorship, self-managed validation, and proactive flagging of gaps before building.

Required Skills and Experience

Bachelor's degree in Software Engineering, Computer Science, or a related technical field.

5+ years of overall software development experience, with at least 3 years actively using AI tools as a core part of the development workflow.

Proficiency in .NET, MS SQL Server (or equivalent RDBMS), MongoDB (or equivalent NoSQL), and API development.

Ability to write structured, machine-precise specs that serve as ground truth for both code generation and output validation.

Practical understanding of prompt construction, context engineering, and diagnosing AI output failures.

Working awareness of the AI tool ecosystem — intelligent system tooling (LLM APIs, RAG, Vector Databases, Agent Frameworks) and SDLC acceleration tooling (Agentic Coding, AI Code Review, AI Test Generation).

Strong written communication skills and experience working in Agile or Kanban environments with end-to-end ownership of deliverables.

Nice to Have Qualities & Skills

Hands‑on experience building RAG pipelines, working with Vector Databases, or integrating LLM APIs into production systems.

Experience with multi-agent orchestration frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.

Familiarity with AI observability tooling such as LangSmith, LangFuse, or Braintrust.

Exposure to spec‑driven development practices such as BMAD or GitHub Spec Kit.

Exposure to Real Estate Technology, Mortgage, or related Financial Services domain.

Awareness of Model Context Protocol (MCP) and its role in connecting AI agents to external tools and data sources.