About the role
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
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