Engineering Manager — AI Engineering

Innovapptive · Hyderabad

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Last observed by our collector: 8 October 2026 at 11:34 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: CuJcygSypD

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Collected role description

Innovapptive is an enterprise SaaS company building an AI-powered Connected Worker Platform for industrial organizations. 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. 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 . 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. This role leads the AI Engineering team responsible for designing, building, and operating that agent portfolio in production. 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. All inference within Innovapptive’s AWS VPC — no data to external LLM endpoints. 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.