AI Delivery Lead

Zoho; Hyderabad; On-site; INR 2,500,000 - 6,000,000; Full time

Job summary

Anthrobyte seeks a hands-on AI delivery leader who works inside client environments to translate messy realities into trusted production systems on our ATLAS platform.

You will own end-to-end delivery, develop reusable patterns, and grow engineers into capable operators while earning client trust through architecture and security reviews.

Qualifications

• Production track record: personally taking AI/ML/agent systems into enterprise production with real client data.
• Proof you build people: mentored engineers, set technical standards, and shaped team practices.
• Range: comfortable coding, whiteboarding architecture, and engaging with client security leaders.
• Systems mindset: focus on reliability, auditability, and sustained operations in enterprise AI.
• Ownership drive: energized by building from early stages and shaping execution.

Responsibilities

• Embedded delivery: work inside the client's environment to understand workflows and constraints firsthand.
• End-to-end ownership: own business problem to deployed, secured, monitored agent running in production.
• The product loop: convert field learnings into reusable patterns and standards within ATLAS.
• Building the team: hire, mentor, and uplift engineers to operate like you and codify your playbook.
• Client trust: engage with security, data, and leadership to earn confidence during reviews.

Skills

Production track record

Mentors engineers

Range

Systems mindset

Ownership drive

Job description

Anthrobyte wins by putting AIagents into the real operations of enterprise clients — and making them work,in production, where it matters. We need a leader who does this exceptionallythemselves and builds a team that does it the same way. You'll own how wedeliver AI, from the client's messy reality to a system they trust — and turnevery engagement into repeatable capability inside our ATLAS platform.

This is a build-from-the-frontrole. You lead from inside the problem, not from a whiteboard.

Whatyou'll own

• Embedded delivery. Yougo where the work is — inside the client's environment, alongside theiroperators — to understand their workflows, data, and constraints firsthand. Youdon't design in the abstract; you build against reality.
• End-to-end ownership. Frombusiness problem to architecture to a deployed, secured, monitored agentrunning in production and driving outcomes. You own the whole arc, not a sliceof it.
• The product loop. Youturn what you learn in the field into reusable patterns, accelerators, andstandards inside ATLAS — so the next engagement is faster and we compound intoa product, not a services treadmill.
• Building the team. Thisis the multiplier. You hire, mentor, and level up engineers to work the way youdo — codifying your instinct into a playbook others can run. Success is a teamwho deliver like you, not you delivering everything.
• Client trust. You sitacross from client security, data, and business leaders and earn theirconfidence — in architecture reviews, security reviews, and the room wheredecisions get made.

Requirements

Whatwe're looking for

• Production track record. You'vepersonally taken AI, ML, or agent systems into enterprise production — notdemos, not pilots that died. You've worked with real client data and lived withwhat you shipped.
• Proof you build people. You'vementored engineers, set technical standards, and shaped how a team works.Evidence you can reproduce your skill in others, not just exercise it yourself.
• Range. You're equallycomfortable writing code, whiteboarding architecture, and holding your own witha client's CISO.
• Systems mindset. Youthink in reliability and trust, not cleverness — enterprise AI is won onauditability and things that keep working.
• Ownership drive. You'reenergised by building something from early and shaping how it's done.

Bonuspoints

• Domain experience insupply chain, procurement, pricing, or other regulated / operational enterpriseenvironments.
• Exposure to enterprisesecurity, governance, and responsible-AI practices.
• A track record of growingengineers into strong, independent operators.

Whatthis role is not

A back-office ML research lead.A pure people-manager who's left the tools behind. An architect who designsfrom a distance and never touches the client's reality. If you want to leadfrom a whiteboard rather than from inside the problem, this isn't it.

Small, high-trust, founder-closeteam. Governance-first, because our work runs inside clients' live operations.You'll have real authority over how we build and deliver — and the mandate toshape the team around strong talent.

Tell us two stories. First: oneAI system you took into enterprise production — the messy problem, how youbuilt and secured it, and what broke along the way. Second: someone you madebetter — an engineer you grew, and how. Those two stories tell us more than anyCV.