Academic Research, Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Caching, Cloud Computing, Computer Programming, Cost Control, Data Analysis, Data Science, Debugging Skills, Distributed Computing, Docker, Economics, MCP - Microsoft Certified Professional, Mentoring, Product Demonstration, Production Systems, Prototyping, Python Programming/Scripting Language, Quality Metrics, Traffic Shaping
Job Description
Must-Have Requirements
Requirement Details
Backend/Systems Experience
3+ years building production backend or distributed systems (pre-AI experience required)
Production AI Systems
Has shipped AI/LLM features serving real users at scale not just prototypes or demos
Agentic Systems
Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
Python
Proficient for backend development
Secondary Language
Working knowledge of Go, TypeScript, or Rust
Cloud Infrastructure
Deep experience with AWS/GCP/Azure cost optimization, compute decisions, not just deployment
Container & Orchestration
Hands-on with Docker and Kubernetes can build, deploy, debug, and scale services themselves
LLM Integration
Understands token economics, context limits, rate limiting, structured outputs, API failure modes
LLM Evaluation
Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
Hands-On Engineer
Not just an architect writes code, debugs production issues, deploys their own work
Preferred / Differentiators
- Built multi-step agentic workflows with tool use and function calling
- Experience with agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, Google ADK, OpenAI ADK)
- Built guardrails, fallbacks, or graceful degradation for AI systems
- Streaming inference and async agent orchestration
- Cost/latency optimization: caching, batching, prompt compression
- ML observability tools: Langfuse, Arize, Braintrust, W&B
- Retrieval systems (vector search, hybrid search) as a tool, not the focus
Screening Questions for Candidates
- "Describe a production AI agent or skill system you built. What broke and how did you fix it? "
- "Have you built MCP servers/integrations or custom tool-use systems for LLMs? "
- "How do you evaluate whether an LLM-based feature is working well? What makes this hard? "
- "Walk me through how you'd deploy and scale an AI service on Kubernetes. "
Not a Fit If
- Primarily a model trainer/fine-tuner (we're not training models)
- AI experience is mainly academic, research, or tutorial-based
- No production systems experience (only notebooks/demos)
- Looking for entry-level role with heavy mentorship
- Background is primarily data science/analytics rather than engineering
- "Architects " who don't write or deploy code themselves