ML Infrastructure Lead

Whitetable • Software • Recruiting Software & ATS • 5 days a week in the office • Python • 1 day ago
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Salary
≈ $25k – $54k per year (Estimated)
Location
In office (Bengaluru, India)
Seniority
Staff · 8+ years exp

First seen by Alion on Oct 7, 2026.

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Whitetable

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whitetable.ai • Bengaluru • Mumbai • Hyderabad • Delhi • Jaipur • Software • Recruiting Software & ATS
Tech stack of Whitetable

ML Infra Lead - Agentic AI

Location: Bangalore / San Francisco

Work Mode: On-site | 5 Days a Week

Experience: 8 - 12 Years

About the Role:


- We are looking for an experienced ML Infra Lead to build and scale the infrastructure behind our Agentic AI platform.

- The ideal candidate should have strong infrastructure and software engineering experience, along with practical knowledge of Agentic Harnesses, Claude Code, and AI coding-agent systems.

Key Responsibilities:

- Build and scale infrastructure for Agentic AI / Agent Harness systems.

- Design reliable and scalable cloud infrastructure.

- Work with AWS/GCP/Azure, Docker and Kubernetes.

- Build systems for running and managing AI agents and their workloads.

- Set up monitoring, logging and observability.

- Optimize infrastructure for performance, reliability and cost.

- Work closely with AI/ML engineers and leadership.

- Debug complex infrastructure and production issues.

- Mentor engineers and drive engineering best practices.

Requirements:

- 8 - 12 years of software/infrastructure engineering experience.

- Strong hands-on experience with cloud infrastructure.

- Strong Python skills.

- Experience with Docker and Kubernetes.

- Good understanding of distributed systems and networking.

- Experience with production monitoring and observability.

- Working knowledge of Agentic AI / Agent Harness architecture.

- Hands-on experience with or strong understanding of Claude Code or similar AI coding-agent systems.

- Should have built or set up Agent Harness / AI agent infrastructure.

What We Look For:

We want someone who can build, not just manage.

- You should be able to explain:

1. What you have built

2. How the architecture worked

3. What went wrong

4. How you fixed it

5. How you would improve an Agentic AI system

Interview Process:

30-min CTO & CEO discussion followed by a 2-day work trial.

Skills

MLOps, Agentic AI, Python, Kubernetes, Docker, Cloud, Machine Learning, Artificial Intelligence, Monitoring Tools, Observability Services, Distributed Systems

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