Engineering Manager — AI Engineering

Engineering Manager - AI Engineeringneering

Location: Hyderabad, India
Employment Type: Full-Time; Salaried
Compensation: Base Salary, Bonus, Stock Options, Medical

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