Engineering Manager - AI Engineering

Engineering Manager - AI Engineering

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 WorkerPlatform for industrial organizations. Our platform connects frontline workers,back-office systems, and assets in real-time to drive safety, reliability, and operationalproductivity.Leading global enterprises including Shell, Hess, Westlake Chemical, Kimberly-Clark,Scott Miracle-Gro, and Newmont Mining, rely on Innovapptive to transform how work getsdone 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 Sullivans “Frost Radar 2025 -Augmented Connected Worker Platforms”, with acknowledgments from Gartner and LNSResearch, 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 $100MARR.

The Role

Innovapptive’s Connected Worker Platform is expanding its AI capability fromfoundational features into a broad portfolio of product-facing AI agents — purpose-builtfor industrial field operations. These agents span maintenance planning, work orderautomation, safety compliance, operator rounds, and knowledge assistance, allgrounded in customer-specific asset data and SOPs.This role leads the AI Engineering team responsible for designing, building, and operatingthat agent portfolio in production. You own the full lifecycle: from architecture andprompt engineering through evaluation, deployment, and reliability. You work closelywith Product, Platform, and customer-facing teams to translate industrial use cases intoAI capabilities that enterprise customers trust.

What You Own

● AI Engineering team: 6–8 engineers 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, chunkingstrategies, embedding, vector search, and knowledge graph grounding.

● LLM governance: model selection, prompt versioning, bias testing, audit logs, andhuman-in-the-loop controls. All inference within Innovapptive’s AWS VPC — nodata to external LLM endpoints.

● Agent quality: evaluation frameworks, accuracy benchmarks, hallucinationmonitoring, and output labelling pipelines.

● Sprint delivery and production reliability. Weekly quality scorecard.

● Hiring, performance management, and coaching. Build the team to full operatingcapacity.

You Must Have

● 7+ years in software engineering with 3+ years managing teams delivering AI/MLor 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, andoutput 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, an…