Lead AI Engineer

Role: Lead AI Engineer

Location: Hyderabad (on-site only)

Experience: 6+ years in AI/ML, with prior team

leadership Reports to: Founder/CEO

About AutomatR

AutomatR is a Unified Agentic Orchestration platform — bringing together Workflows,

Agentic AI, Agentic Document Extraction, and Agentic RAG with Human-in-the-Loop, all

governed through a centralized AI Gateway that handles governance, guardrails, agent

discovery, and a centralized MCP server. We serve enterprise customers across regulated

industries, deployed across cloud, on-prem, and air-gapped environments. Our agentic AI

capability is a core differentiator of the platform.

The Role

We're looking for an AI Leader to own the technical vision and execution ofAutomatR's

agentic AI capabilities end-to-end — Agentic Document Extraction, Agentic AI, Agentic

RAG, and the governance/infrastructure layer underneath all of it. This is both a hands-on

technical role and a leadership one: you'll make architecture calls, guide a growing AI

engineering team, and be the final word on AI technical decisions the founder currently has

to make personally.

You'll also be a critical bridge in the org: our .NET engineering team and ourAI team don't

naturally speak the same language today. Part of this role is ensuring agentic AI capability

integrates cleanly into the broader platform — not built in isolation — which means

working closely with the Tech Lead and .NET engineering to make sure AI features are

usable, deployable, and maintainable across the full product.

What You'll Do

• Own the technical roadmap and architecture forAutomatR's agentic AI stack: Agentic
• Document Extraction, Agentic AI, and Agentic RAG with Human-in-the-Loop
• Make and defend architecture decisions — model selection, cost-vs-quality trade-offs, and how agentic systems are governed, monitored, and controlled in production Own the AI Gateway

layer — centralized governance, guardrails, agent discovery, and MCP server infrastructure that keeps agentic capability safe, observable, and consistent across the platform Lead, mentor, and grow the AI engineering team; set technical standards and review

• AI-generated and human-written code alike Partner with the Tech Lead and Product Owner to translate product specs into AIfeasible technical plans, and push back with technical reality when scope and feasibility don't line up
• Evaluate and integrate new models, frameworks, and agentic AI techniques as the field moves — separate real capability gains from hypeEnsure AI systems meet the reliability, auditability, and data-handling bar required by regulated enterprise customers
• Own AI infrastructure cost efficiency — balance quality against real serving cost at scale

WhatWe're Looking For

• 6+ years of hands-on AI/ML engineering experience, including production systems — not just research or prototyping
• Direct experience building agentic AI systems and Agentic RAG in production, not Just prototypes or demos
• Prior experience leading or mentoring an AI engineering team, with real accountability for technical outcomes
• Strong architectural judgment — comfortable owning trade-offs like model choice vs. cost vs. accuracy, and defending those calls with data
• Able to operate at both altitudes: deep enough to review technical work and unblock hard problems, senior enough to own roadmap and represent AI strategy to the founder and to customers
• Comfortable working across a mixed .NET/Python organization — translating AI
• capability into terms the broader engineering team and product org can build around
• Track record of shipping AI features into production, not just demos — including the discipline of validating in a sandbox before committing to production architecture

Nice to Have (not required)

• Experience with multi-agent orchestration and agent governance frameworks
• Experience deploying AI systems in regulated industries (pharma, financial services,
• healthcare) or in on-prem/air-gapped environments
• Experience with document understanding / document AI pipelines
• Exposure to infrastructure planning and cost optimization forAI systems at
• production scale
• How You'll Be Evaluated in the Interview
• Expect a deep architecture discussion on a real production agentic AI system you've built — including the trade-offs you made and why — plus a discussion on how you'd structure
• and grow an AI engineering team inside a broader organization that isn't AI-native.