AI Delivery Lead

Anthrobyte; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Anthrobyte seeks a leader who can drive AI delivery inside enterprise environments, owning the problem from field to production. You will build a capable team and codify patterns to turn engagements into repeatable capability within the ATLAS platform.

You will operate inside client environments, ensuring security, reliability, and measurable outcomes, while shaping how the product evolve based on field learnings.

Qualifications

• Proven track record of taking AI/ML/agent systems into enterprise production.
• Experience mentoring engineers and shaping how a team works.
• Comfortable writing code, whiteboarding architecture, and engaging with CISO-level stakeholders.
• Think in reliability, auditability, and long-term operations of enterprise AI.
• Demonstrated ability to build scalable processes and patterns from field learnings.

Responsibilities

• Lead embedded delivery inside client environments to understand workflows and data.
• Own end-to-end architecture to deployed, secure agents in production.
• Turn field learnings into reusable patterns inside ATLAS to accelerate future engagements.
• Build and scale the engineering team, codifying playbooks for others to use.
• Establish client trust through architecture and security reviews with stakeholders.

Skills

Production track record

Mentor engineers

Cross-functional teamwork

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.

What you'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

What we'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.

Bonus points

• 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.

What this 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.