Job Description
Description
You’ve built agentic systems. You’ve shipped LLM-powered features to production. You know the difference between a cool demo and something that actually works at scale.
And yet… somehow you’re still spending most of your day writing CRUD endpoints.
We’re hiring an AI Native Staff Engineer at Carv. We build agentic AI systems in the recruitment space — and we need someone who’s done this before, not someone who’s just getting started.
What You’ll Do
Build AI-Native Product Systems
Design and ship AI-powered product features end-to-end
Architect systems where LLMs, agents, and traditional software work together
Implement RAG pipelines, structured reasoning flows, and tool-using agents
Continuously improve reliability, latency, and cost efficiency
Engineer With AI at Full Leverage
Use AI agents to accelerate development, testing, and architecture decisions
Prototype rapidly and ship production-grade systems
Set up internal AI tooling that multiplies team output
Push the boundary of what’s possible with current models
Own Impact, Not Experiments
Translate product problems into scalable AI-powered solutions
Measure real-world performance (accuracy, business impact, UX)
Optimize for production robustness —> not just demo quality
Shape Our AI-Native Engineering Culture
Raise the bar for how we use AI internally
Establish pragmatic standards for evaluation and iteration
Mentor engineers on AI-native workflows
Contribute to long-term technical direction
What We’re Looking For
You:
Actively use LLMs in your daily workflow
Have built agents, RAG systems, or AI-powered tools
Care about practical reliability over theoretical elegance
Think in orchestration, not just prompts
Experiment constantly with new models and tools
We care less about:
Academic ML background
Publishing papers
Training models from scratch
We care more about:
What you’ve shipped
How you use AI to move faster
How you think about systems that include AI components
Why Join Us
We’re redefining recruitment with AI at the core of the product — not bolted on.
You’ll:
Work directly with experienced SaaS leadership (15+ years building in this space)
Have real ownership over architecture and AI direction
Built in a high-velocity, high-impact environment
Help define what AI-native enterprise software looks like
What’s in It for You
Very competitive compensation package
Meaningful stock options
Top-tier tools (MacBook Pro + AI tooling budget)
Hybrid setup + occasional travel
Really make an impact
Requirements
Staff-Level Engineering Depth
8+ years of professional software engineering experience
Proven track record in designing and shipping large-scale production systems
Experience owning architecture across services or product domains
Strong backend engineering fundamentals (APIs, distributed systems, data modeling, concurrency, reliability)
Experience operating systems in production (monitoring, incident handling, performance tuning)
Cloud-native experience (GCP, AWS, or Azure)
Production AI Systems (Not Research)
Experience shipping LLM-powered features to real users in production
Designed and implemented RAG systems in production environments
Built or architected AI agents or tool-using multi-step reasoning systems
Designed evaluation frameworks for LLM output quality, safety, and regression detection
Experience optimizing AI systems for latency, cost efficiency, and reliability
Experience integrating vector databases and embedding pipelines into scalable systems
AI-Native Engineering Approach
Actively use AI tools and agents to augment your engineering workflow
Demonstrated ability to design systems where AI components and deterministic systems work together
Experience turning fast AI prototypes into production-grade systems
Strong judgment around when to use AI vs deterministic logic
Ownership & Impact
Experience leading complex technical initiatives end-to-end
Ability to translate ambiguous business problems into system architecture
Experience mentoring senior engineers or setting technical standards
Track record of shipping high-impact features with measurable outcomes
