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Estimated pay
Estimate$220K–$315K/yr
Estimate — the employer hasn't published pay.
Based on 77 similar manager-level Data Science & ML jobs in United States that show pay. The range is the middle half of their posted pay (25th to 75th percentile), not an offer — ask the employer for the real range.
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At a glance
- Education
- Master's
About the role
AI Platform Lead
This job is half product, half engineering: you decide what the AI Platform should do, then design and own the shared platform that makes it work reliably.
You will be the first engineer dedicated to this platform. Nothing shared exists yet — you design it.
Hands-on architecture and building. This is not a management role.
What you will own
1. What each Virtual Agent should do
2. The platform architecture
3. Reliability
4. Making it easy to build on
Design reusable AI workflows for real business problems across product, content, growth, people and finance.
Make it straightforward for another engineer to add a new workflow, a new tool, a new Virtual
Agent or a different AI provider — without rebuilding the foundation.
Write the standards down, and be honest in writing about trade-offs.
5. Adoption
Review new Virtual Agent proposals so the fleet stays consistent and we do not solve the same problem twice.
Coach the team directly, so working with AI becomes normal here rather than exceptional.
Success is measured by teams working differently, not by tools installed.
Who we are looking for
Essential
Senior software engineering experience, typically 7+ years, including time as a lead or architect.
You have built AI agent or AI platform systems that ran in production under real load, and you can talk openly about what broke and what you changed.
Other engineers built on what you built. We are hiring a platform owner, not the author of one impressive agent.
Genuine product instinct. You start with the user and the job to be done, not with the architecture. You can frame a problem and write a short brief that a non-engineer understands and acts on.
Curiosity about other people's work. You are comfortable sitting with a recruiter, a designer or a marketer, watching how they actually work, and finding the problem worth solving. You do not need prior experience in those functions — you need the instinct to go and learn them.
Hands-on depth in modern AI systems: LLMs, agents, tool use, orchestration, retrieval, memory and evaluation.
Real judgement about reliability, cost, speed and scale in production, not only in prototypes.
You want to stay close to the code. This is a hands-on role.
Professional English, written and spoken.
Customer First — decisions grounded in data and real empathy for the user's problem, colleagues included.
Be on Stage — do what you say, speak up, give and take direct feedback, and deliver results rather than finish tasks.
Originally posted on Himalayas