Engineering Manager (AI)

About Innovapptive

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.

You Must Have

• 7+ years in software engineering with 3+ years managing teams delivering AI/ML or LLM-powered products in enterprise production.
• Hands-on experience with LLM orchestration frameworks (LangGraph, LangChain, or equivalent) and multi-step agentic workflows.
• Strong grasp of RAG architecture: document pipelines, chunking, embedding, vector databases, re-ranking, and similarity thresholds.
• Experience with managed inference infrastructure: AWS Bedrock, SageMaker, or equivalent.
• Track record shipping AI product features on schedule in a SaaS context — not just prototypes or internal tools.
• Familiarity with AI observability: prompt tracing, hallucination detection, and output evaluation (Langfuse, Ragas, or equivalent).
• Data-driven: model evaluation scores, accuracy/recall metrics, agent success rates, and DORA metrics for the team.
• Strong engineering standards: prompt discipline, eval-driven development, responsible AI controls, and production-grade reliability.

Nice to Have

• Knowledge graph architectures (AWS Neptune, Neo4j) for grounding agent outputs in structured asset data.
• Industrial domain knowledge: EAM, ERP integrations (SAP, Maximo), maintenance workflows, or field operations.
• Multi-agent orchestration patterns: tool calling, agent-to-agent delegation, and human-in-the-loop checkpoints.
• Vision models or multimodal AI: image-based defect detection, document OCR, or form digitisation.
• MLOps and LLMOps: model versioning, A/B evaluation, and continuous prompt optimisation pipelines.
• Cloud cost optimisation for LLM workloads: token budgets, model tiering, and caching strategies.
• MongoDB and change stream-based event architectures.

Tech Stack & Tools

1. AI / ML- AWS Bedrock, SageMaker, LiteLLM, …