Engineering Manager — AI Engineering

Role overview

Engineering Manager — AI Engineering at Innovapptive

This role leads the AI Engineering team responsible for designing, building, and operating that agent portfolio in production.

Employment details

• Location: Hyderabad [India]

About the employer

• Innovapptive is an enterprise SaaS company building an AI-powered Connected Worker Platform for industrial organizations.
• Innovapptive is recognized as a Leader in Frost & Sullivan's “Frost Radar 2025 - Augmented Connected Worker Platforms”, with acknowledgments from Gartner and LNS Research, and is backed by Vista Equity Partners and Tiger Global Management .
• All inference within Innovapptive’s AWS VPC — no data to external LLM endpoints.

Additional role details

• Our platform connects frontline workers, back-office systems, and assets in real-time to drive safety, reliability, and operational productivity.
• Leading global enterprises including Shell, Hess, Westlake Chemical, Kimberly-Clark, Scott Miracle-Gro, and Newmont Mining, rely on Innovapptive to transform how work gets done across plants and field operations.
• Our customers have achieved $50M+ EBITDA savings at a single enterprise, 10× improvement in frontline productivity , and 15–20% reductions in maintenance costs.
• With headquarters in Houston and an engineering center in Hyderabad, we have 300+ employees across the U.S., India, and ANZ and are on a strong trajectory toward $100M ARR.
• The Role Innovapptive’s Connected Worker Platform is expanding its AI capability from foundational features into a broad portfolio of product-facing AI agents purpose-built for industrial field operations.
• These agents span maintenance planning, work order automation, safety compliance, operator rounds, and knowledge assistance, all grounded in customer-specific asset data and SOPs.
• You own the full lifecycle: from architecture and prompt engineering through evaluation, deployment, and reliability.
• You work closely with Product, Platform, and customer-facing teams to translate industrial use cases into AI capabilities that enterprise customers trust.
• What You Own AI Engineering team across agent development, LLM infrastructure, and model evaluation.
• End-to-end agent lifecycle: requirements through architecture, build, evaluation, deployment, and production monitoring.
• RAG and knowledge infrastructure: document ingestion pipelines, chunking strategies, embedding, vector search, and knowledge graph grounding.
• LLM governance: model selection, prompt versioning, bias testing, audit logs, and human-in-the-loop controls.
• Agent quality: evaluation frameworks, accuracy benchmarks, hallucination monitoring, and output labelling pipelines.
• Sprint delivery and production reliability.
• Weekly quality scorecard.
• Hiring, performance management, and coaching.