Role : Software Developer
Location : REMOTE
Job Type : W2 Contract
EX AMAZON IS the Goal here but will take FAANG, MANGO, Magnificent 7
Needs;
Python
AI Agentic Application Creation.
Job Description:
Key Shift in Profile (Most Important)
This is NOT a traditional full stack or backend role anymore.
Ross is prioritizing candidates who have:
Built end-to-end AI / agentic applications
Understand what goes into an LLM, what comes out, and how to use it
Can productionize AI systems (not just prototype)
What Ross Is Actually Looking For
1. End-to-End Agentic AI Builders
Candidates must be able to speak to:
- How they designed and built an AI application from scratch
- What data/input goes into the LLM
- How outputs are structured, interpreted, and used
- How the application functions as a complete system
This is not theoretical - must be hands-on, real experience
2. Ability to Turn LLM Output into Action
Key expectation:
"Don't just call an LLM - do something meaningful with the output"
Candidates should demonstrate:
- Structured output handling
- Workflow orchestration
- Turning AI responses into usable application features
3. Productionization > Prototyping
This is where candidates are currently falling short.
Ross is heavily focused on:
- How AI apps are moved into production environments
- Understanding of:
- CI/CD in enterprise environments
- Deployment workflows
- Reliability & operational considerations
They don't have to build CI/CD pipelines themselves
But must understand how production systems work at a high level
4. Deep Understanding of Agentic Systems
Candidates must be able to confidently discuss:
- Memory management in agentic applications
- How agents:
- Maintain context
- Store/retrieve information
- Execute multi-step workflows
If they've actually built agentic systems, they should answer this easily
5. Strong Python Focus (Critical Update)
- Python is the primary language
- TypeScript is acceptable but secondary
- Java is not required
Reason:
- Most agentic frameworks and tooling are Python-based
- Compute + memory patterns align more closely in Python ecosystems
6. AI Framework Awareness (Not Tool-Specific)
Ross does NOT require specific tools, but expects:
- Awareness of the agentic/LLM ecosystem
- Ability to articulate:
- Tradeoffs between frameworks (LangGraph, etc.)
- Personal preferences and why
He cares about opinion + understanding, not tool matching
7. Engineering Balance (Important Gap Area)
Two common failure patterns:
Strong data science candidates
Lack ability to build real applications
Strong engineers
Lack real GenAI / agentic depth
Ideal candidate = balanced profile
- Solid engineering fundamentals
- Real GenAI + agentic application experience
8. LLM Gateway Experience = Nice, Not Core
- Building gateways / API layers is ancillary
- Core focus = application layer + agent behavior + output usage
What Candidates Are Missing Today
Based on Ross's feedback, most candidates are lacking:
- True end-to-end ownership of AI applications
- Ability to explain:
- "How did you actually use the LLM output?"
- Production experience with AI systems
- Depth in agentic concepts (memory, orchestration)
Interview Process Update
- 2 rounds total
- Round 1: Ross (scenario/system discussion)
- No coding test
- Focus areas:
- System thinking
- Agentic architecture
- Real-world application experience
- Communication clarity
Updated Candidate Profile (Target)
Strong candidates will:
- Have built AI-powered or agentic applications end-to-end
- Be fluent in:
- LLM inputs/outputs
- Prompt orchestration
- Workflow execution
- Understand how to productionize AI systems
- Be Python-first engineers
- Speak confidently about:
- Memory management
- Agent behavior
- Design tradeoffs
Action for Recruiting Team
Tighten Screening Immediately
You should now prioritize candidates who can clearly answer:
- "Walk me through an AI/agentic application you built end-to-end"
- "What did you feed into the LLM, and what did you do with the output?"
- "How did you move that system into production?"
- "How did you manage memory or context in your agent?"
- "What agentic frameworks have you used or evaluated, and why?"
Deprioritize Candidates Who:
- Only have surface-level GenAI exposure
- Cannot explain production deployment
- Are heavily Java-focused without AI depth
- Come from pure data science without app development
Bottom Line
This role is now clearly:
A senior engineer who builds and ships real agentic AI applications - not someone experimenting with LLMs or building backend services alone.
Email- Phone Number : +1 321 7856 062
STELLENT IT A Nationally Recognized Minority Certified Enterprise
"Happiness can be found, even in the darkest of times, if one only remembers to turn on the light."
- JK Rowling
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