全职 Python Developer - Agentic AI Platform 工作, 薪水, Two95 International Inc. Pulau Pinang 公司招聘中 - Ricebowl

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
• Build 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