Head of Ai Engineering

ABOUT KALO TECH STUDIO

Kalo Tech Studio is a digital transformation and AI company headquartered in Hyderabad. We partner with enterprises across industries to build AI-native systems from first principles — not as a feature layered onto existing processes, but as the foundation every workflow is built upon.

At Kalo, AI is not a product feature. It is the substrate. We are growing fast and looking for leaders who think the same way.

THE ROLE

We are looking for a Head of AI Engineering — the most senior technical voice on all AI/ML initiatives at Kalo. This is a high-ownership role at the intersection of business strategy, technical architecture, and engineering delivery.

You will work directly with the CXOs & attend client-facing discovery and strategy sessions, translate complex business requirements into precise AI architecture decisions, and lead your team to deliver end-to-end — from proof of concept to production-grade systems.

This is not a management-only role. We want a builder who leads. You should be as comfortable writing an architecture decision record as you are presenting a transformation roadmap to a client’s executive committee.

WHAT YOU WILL DO

Client & Business Engagement

•     Lead and participate in client-facing discovery sessions alongside senior leadership — understanding business objectives, pain points, and AI transformation opportunities firsthand.

•     Translate complex, often ambiguous client requirements into precise AI/ML solution architectures and phased delivery roadmaps.

•     Present AI strategies, technical recommendations, and implementation plans to client stakeholders, technical teams, and executive leadership.

•     Build trusted relationships with client business and technical teams throughout the entire project lifecycle.

•     Identify new AI opportunity areas within existing client engagements and help articulate their business case.

AI Architecture & Technical Leadership

•     Own end-to-end AI/ML architecture across projects — data pipelines, model selection and evaluation, agent design, deployment infrastructure, monitoring, and governance.

•     Design and implement Generative AI solutions: large language model architectures, retrieval-augmented generation systems, multi-agent orchestration frameworks, and multimodal AI pipelines.

•     Architect intelligent automation and decision-support systems that embed AI into live business processes — financial monitoring, supply chain intelligence, field force analytics, and scientific knowledge management.

•     Build enterprise knowledge systems combining structured data, unstructured documents, and vector search — enabling natural language querying across complex organisational data estates.

•     Design and enforce AI safety standards: explainability requirements, confidence scoring, hallucination mitigation, human-in-the-loop escalation paths, and responsible AI governance frameworks.

•     Evaluate and make deliberate technology decisions across frameworks, cloud platforms, hosting models, and AI deployment strategies — and document the reasoning clearly.

•     Stay ahead of emerging AI/ML research and tooling; identify what is genuinely applicable to client and product contexts versus what is noise.

End-to-End Delivery

•     Own AI/ML project delivery from architecture through to production — on time, to quality, and within scope.

•     Collaborate with full-stack engineers, data engineers, product managers, and business analysts to embed AI capabilities into live enterprise systems.

•     Ensure all models and agents are production-ready: versioned, monitored, observable, and continuously improved with drift detection and retraining pipelines.

•     Identify delivery risks early, communicate them clearly, and drive resolution — with solutions, not just flags.

•     Define MLOps standards across the team: CI/CD for ML pipelines, model registries, evaluation benchmarks, and deployment governance.

Team Building & Capability Development

•     Hire, mentor, and grow a high-performance AI/ML engineering team.

•     Conduct architecture reviews, technical evaluations, and knowledge-sharing sessions across engineering functions.

•     Build internal AI capability frameworks: training programmes, tooling standards, evaluation benchmarks, and engineering best practices.

•     Drive a culture of technical rigour, curiosity, ownership, and delivery accountability across …