Forward-Deployed AI Engineer
Share your background, links to real work, and how you build, test, and ship AI systems.
The basics
Full name
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First Name
Last Name
Email address
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example@example.com
Phone number
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Please enter a valid phone number.
Format: (000) 000-0000.
GitHub, portfolio, or personal site
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LinkedIn profile
CV or resume link
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Work type
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Full-time
Contract
Either works
Base salary range
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Start date timing
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Immediately
Within 2 weeks
Within a month
2 months or more
Proof of work
Built platforms people actually used
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Most autonomous system you built
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3–5 minute screen recording link
Where you sit
Be honest rather than generous. We would rather know where the gaps are than be surprised in week two.
For each area, pick the level that honestly describes you today.
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Rows
Never done it
Can follow a pattern
Ship independently
Own the design
Set the standard
Frontend and UI
Backend services and APIs
Infrastructure and deployment
Building with LLMs
Agentic automation and workflows
Working directly with clients
What you have actually shipped
Q15. What have you actually shipped?
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Given a model tools or functions it can call
Handled a tool call that failed or returned nonsense
Built retry or fallback logic for when a model gets it wrong
Forced structured output and validated it before using it
Kept state or memory across a multi-step run
Chained more than three model calls in one workflow
Put a human approval step inside an automated flow
Set spend, rate or loop limits on an AI feature
Written evaluations that run automatically
Caught a quality regression before users did
Handled untrusted input reaching a model
Debugged why an agent took the wrong action
Reduced token cost or latency on a live feature
Replaced an AI approach with plain code because it worked better
Handed an AI system over to a client and supported it
None of these yet
Q16. Which AI tools and platforms have you used?
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OpenAI API
Anthropic or Claude API
Google Gemini
Open-weight models (Llama, Mistral, Qwen)
Local inference (Ollama, vLLM, llama.cpp)
RAG and retrieval
Vector databases
Reranking or hybrid search
LangChain or LlamaIndex
LangGraph or another agent framework
MCP (Model Context Protocol)
Tool and function calling
Structured output and schema enforcement
Fine-tuning
Classical ML
Speech (Whisper, TTS)
Vision or document extraction
Evaluation tooling (LangSmith, Braintrust, Ragas, or your own)
Guardrails or prompt injection defence
n8n, Make or Zapier
Temporal, Airflow or Prefect
Browser automation (Playwright, Puppeteer)
Agent orchestration I wrote myself
Other
Q17. Which languages, frameworks, and infrastructure have you worked with?
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TypeScript or JavaScript
Python
Go
Another backend language
React or Next.js
Another frontend framework
Turning designs into working UI
REST API design
PostgreSQL or another relational database
Redis or caching
Queues and background jobs
Auth (OAuth, SSO, JWT)
Docker
Kubernetes
CI/CD pipelines
AWS
GCP
Azure
Serverless
Infrastructure as code (Terraform, Pulumi)
Monitoring and tracing
None of these
Other
Q18. Which AI coding tools do you use?
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Claude Code
Cursor
GitHub Copilot
Windsurf
Cline or Aider
ChatGPT or Claude in the browser
None
Other
How you think about AI
Q19. How do you decide what the model should handle versus plain code?
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Q20. How do you know an AI feature is working, and how would you detect if it quietly stopped?
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Q21. Tell us about an AI feature that went wrong in front of a user: what happened, the actual cause, and what changed afterward.
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Q22. How do you decide whether a messy manual client process needs an agent, a simple script, or neither?
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Q23. Optional: What have you automated that used to take a person hours, how much time did it save, and what still needs a human?
Last few things
Current or most recent role: hands-on building percentage
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90% or more
70% to 90%
50% to 70%
Less than 50%
Strongest area and current growth area
Anything else we should know
Confirmation
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I confirm these answers describe work I personally did, and I consent to Zare Innovations storing this application for recruitment purposes.
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