Full Stack Python Developer - Agentic AI Platform

Location: George Town, Penang, Malaysia

Workplace: on_site

Employment Type: full

Description

Job Description

Overview

We are hiring a Mid-to-Senior Full Stack Developer to build and extend an Agentic AI platform with strong focus on LLM workflows, orchestration, and backend systems. This role requires a highly independent engineer who can quickly understand existing codebases and deliver production-quality enhancements with minimal guidance.

Core Hiring Bar (Non-Negotiable)

• Ability to independently read, understand, and modify moderately complex Python modules (~300+ lines)
• Comfortable filling knowledge gaps through documentation and reasoning (not dependent on AI-assisted coding tools)
• Demonstrated ability to interpret existing systems and implement changes with minimal onboarding

Key Responsibilities

• Develop and enhance backend services and agentic workflows for the AI platform
• stateful, multi-step LLM pipelines and orchestration logic
• Design, optimize, and maintain retrieval and scoring systems
• Debug production issues using logs, traces, and system behavior
• Collaborate across teams to deliver scalable and reliable AI-driven solutions
• Implement incremental changes with strong testing and validation practices

Technical Requirements

Python (Senior Level)

• Writes clean, idiomatic Python using: 
• Type hints, dataclasses, Pydantic
• Generators and context managers
• Strong understanding of: 
• Async/await and concurrency models
• Proficient in Python standard libraries (e.g., pathlib, json, re, collections)
• Able to modify existing complex systems independently

Backend Development (FastAPI)

• Experience building and extending FastAPI services
• Strong understanding of: 
• Request lifecycle
• Dependency injection and middleware
• Multi-worker deployments and shared state (e.g., Redis)
• Able to diagnose issues using logs and traces (minimal debugger reliance)

LLM Engineering (Applied)

• Experience building production-grade LLM workflows
• Strong in: 

• Deterministic prompt design (structured outputs, low/no temperature)
• Handling failure modes (timeouts, malformed outputs)

• Understanding of RAG systems: 
• Chunking, embeddings, similarity scoring

Workflow Orchestration (LangGraph or Equivalent)

• Experience with stateful orchestration frameworks preferred
• Must be able to quickly: 
• Learn graph/state concepts
• Implement multi-step workflows within 1–2 weeks

Retrieval & Scoring Systems

• Experience with ranking/scoring methods (e.g., BM25, hybrid search)
• Ability to tune: 
• Thresholds, weighting, precision vs recall trade-offs
• Capable of building realistic test datasets

Diagnostics & Log Processing

• Familiar with log ingestion and analysis pipelines
• Understands: 
• Chunking strategies
• Pattern extraction vs LLM reasoning
• When to use deterministic vs AI-based parsing

Infrastructure & Runtime

• Hands-on experience with: 
• Docker / Docker Compose (volumes, dependencies, health checks)
• Debugging container runtime issues
• Working knowledge of: 

• Redis (basic operations, TTL, persistence)
• Enterprise networking concepts (e.g., proxies)

Frontend (Working-Level)

• Ability to work with HTML + Vanilla JavaScript
• Comfortable with: 
• DOM manipulation
• Fetch APIs and event handling
• Able to implement UI changes from requirements (no design dependency)

Work Style Expectations

• Strong code-first discipline (understands design before coding)
• Built-in focus on: 
• Testing and validation
• Log-driven debugging
• Writes clean, incremental changes with clear commits
• Performs self-review against acceptance criteria
• Asks focused, implementation-driven questions

Experience & Seniority

• Level: Mid to Senior Engineer
• Experience: 
• ~4–8 years in Python development
• Proven track record delivering production systems
• Experience with LLM-based or AI platforms preferred

Ideal Candidate Profile

• Highly independent problem-solver
• Strong systems thinker (not just feature coder)

• Comfortable working in low-AI-assist environment
• Bias toward execution, debugging, and delivery quality