Founding AI Engineer - Gurugram
ClanX (company site, opens in a new tab)3+ yrsGurugramOn-siteDirect career pageEst. 20–35 LPA
Posted 18h ago
Reach people at ClanX
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at ClanX you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own AI agent architecture and production systems that execute users’ multi-step requests, including tool use, memory, scheduling, failure recovery and completion verification.
- Build browser/computer-use agents for real-world tasks across third-party websites, including login and OTP flows.
- Create evaluation and observability systems to measure reliability and improve performance using task outcomes; own deployment and scaling, working directly with founders on technical and product decisions.
Minimum Requirements
- Work Experience Required: 3+ years in AI engineering, machine learning engineering, applied AI or a related role.
- Currently working at a product-based company; prior founding or early-stage engineering experience is required by the JD, preferably taking a product from 0 to 1.
- Hands-on production LLM-agent experience, browser/computer-use agents (Playwright, CDP or similar), and agent evaluation/observability; strong Python, including asyncio and FastAPI or Django, plus production AWS experience (ECS, SQS or Celery, autoscaling, CloudWatch and CI/CD).
- Must be open to working in-office in Gurgaon six days a week.
Ideal Candidate Profile
- Has operated in a founding or early-stage engineering team and can take AI systems from initial architecture through production deployment and scaling.
- Has built agents for real-world, multi-step tasks—not only prototypes—with recovery, task resumption and verification of outcomes.
- Fits a hands-on product environment where agent quality is assessed through evaluation, observability and real-world task feedback.