Junior FDE (AI & Backend)

Junior FDE (AI & Backend): 1-3 Years of Experience

Context

We are building AI-native products on the Anthropic API and MCP, backed by a Django/DRF + PostgreSQL platform and a React/Next.js frontend, deployed on Azure. The team works in an AI-assisted workflow (agentic AI coding tools are a daily tool), so the Junior FDE must be able to review, understand and own every line that ships. This is a build-and-learn role: you work from tickets a senior has scoped, with code review on every change, and grow into owning full features. 

What the role owns

build

The actual workload (by share of time);

Backend features & APIs (~35%)

DRF models, serializers, viewsets, migrations on PostgreSQL

• e.g. "Add a filter + pagination to the cases list API"

• e.g. "New field with migration and serializer"

LLM workflows, skills & MCP connectors (~25%)

Prompts, tool use, agent skills, custom MCP tools/connectors

• e.g. "Add an MCP tool that fetches a matter's documents"

• e.g. "Write a skill for the intake checklist"

• e.g. "Tighten the extraction prompt"

Async jobs & integrations (~15%)

Celery/Redis tasks, third-party APIs, auth (JWT/OAuth2)

• e.g. "Move document summarisation to a Celery task with retry"

Bugs & debugging (~15%)

• e.g. "Task stuck in pending — find why"

• e.g. "500 on empty payload"

Tests, review & light frontend (~10%)

pytest, PR cycles, small React/Next.js fixes

• e.g. "Add tests for the new endpoint"

• e.g. "Wire the new API into the Next.js list page"

Priority reality:

Tech stack

• AI/LLM engineering:

   Anthropic API, MCP (Model Context Protocol), agentic AI coding tools (CLI/IDE), reusable agent skills (SKILL.md-style), custom agents/subagents, custom MCP connector creation, prompt engineering, basic LLM agent concepts. Optional: OpenAI API, RAG pipelines. 

• Backend:

   Python, Django, Django REST Framework, PostgreSQL, Celery, Redis, JWT/OAuth2, Docker. 

• DevOps/Tools:

   Azure, Git, CI/CD. Optional: Sentry, AWS. 

• Testing & Quality (optional):

   pytest, TDD. 

• Frontend:

   React/Next.js, TypeScript, REST API integration. 

Preferred qualifications

Must-have

• Python + Django/DRF fundamentals:

   1–3 years of hands-on work (internships, projects, open source count). Models, serializers, viewsets, migrations. 

• SQL & PostgreSQL:

   relational modelling, joins, indexes at a basic level. 

• LLM API experience:

   built at least one project calling an LLM API (Anthropic preferred) with tool/function calling — shown on GitHub or in a demo. 

• Skills, agents & MCP basics:

   has written at least one agent skill (SKILL.md-style) or custom agent/subagent, and built a simple custom MCP server/connector (Python or TypeScript) exposing a few tools. 

• API basics:

   REST design, JWT/OAuth2 authentication, Docker basics. 

• Git discipline:

   branching, pull requests, resolving conflicts. 

• Communication:

   clear written updates; asks early when blocked; takes review feedback well. 

Strongly preferred

• Agentic AI coding tools

   used day to day, with a view on where generated code needs extra review. 

• Custom MCP connector creation:

   built and tested an MCP server wrapping a real API, with auth and clear tool descriptions. 

• Agent skills & custom agents:

   authored skills with references/scripts, or configured custom agents with scoped tools. 

• Celery/Redis

   on a background-job feature. 

• React/Next.js + TypeScript

   for small UI work. 

Nice-to-have:

This is first and foremost a backend + AI-workflow hire.

Weight evaluation toward Python/Django fundamentals, problem solving and real LLM projects — not framework trivia.