Senior Data Scientist / AI Engineer

Role : Lead of AI Engineering (Agentic Systems)

Location · Hyderabad (on-site)

Exp: 6 to 8 Years ( AI/ML)

Reports to Founder/CEO [Full-time]

Why this role exists

Today, every significant AI architecture decision at AutomatR runs through the founder. That’s a bottleneck we want to remove by hiring the person who makes those calls. You’ll be the final technical authority on how our agentic AI is designed, governed, and shipped - with the mandate, the team, and the founder’s backing to do it.

What we build

AutomatR is an enterprise AI orchestration platform. Customers in regulated industries - [pharma, financial services, healthcare] - use it to run document extraction, RAG, and multi-step agent workflows with humans in the loop, governed by a central AI Gateway (guardrails, observability, agent discovery, MCP server). We deploy to cloud, on-prem, and air-gapped environments, which makes the engineering problems unusually hard and unusually interesting.

What you’ll own

• Architecture and roadmap for the agentic stack - Agentic Document Extraction, Agentic AI workflows, and Agentic RAG with human-in-the-loop. You make the model-selection, cost-vs-quality, and governance calls, and defend them with data.
• The AI Gateway - the governance layer every AI feature passes through: guardrails, monitoring, agent discovery, and the centralized MCP server. You set the technical direction; [Platform/DevOps] owns deployment tooling alongside you.
• The team - lead, mentor, and grow our AI engineers. Set the technical bar, run design reviews, and review code (human- and AI-written) with the same rigor.
• Integration with the core platform - our platform is .NET; our AI is Python. The two teams work well separately and need someone who can make them work well together. You’ll partner with the Tech Lead and Product Owner to turn product specs into feasible AI plans, and push back with technical reality when scope and feasibility don’t line up.
• Production readiness for regulated customers - reliability, auditability, data handling, and serving-cost efficiency at enterprise scale.

What you bring

• 6 to 8+ years of hands-on AI/ML engineering, with at least 2 years shipping LLM-based or agentic systems to production customers
• Direct experience building and operating Agentic RAG or multi-agent workflows in production - including the sandbox-validate-then-commit discipline
• Led or mentored an AI engineering team with real accountability for outcomes
• Architectural judgment you can defend: model choice vs. cost vs. accuracy, with numbers
• Operates at two altitudes - deep enough to unblock a hard problem, senior enough to represent AI strategy to the founder and to customers
• Comfortable in a mixed .NET/Python organization; can translate AI capability into terms the wider engineering and product teams can build around

Strong advantage

• Document understanding / document AI pipelines
• Deployments in regulated industries or on-prem/air-gapped environments
• Agent governance frameworks or MCP-based architectures
• AI infrastructure cost planning at production scale