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Engineering Manager - AI Engineering

innovapptive

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.

About innovapptive

Manager - AI Engineering neering 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.

Responsibilities

  • This role leads the AI Engineering team responsible for designing, building, and operating that agent portfolio in production.
  • 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.
  • You Own AI Engineering team across agent development, LLM infrastructure, and model evaluation.
  • requirements through architecture, build, evaluation, deployment, and production monitoring.
  • document ingestion pipelines, chunking strategies, embedding, vector search, and knowledge graph grounding.
  • model selection, prompt versioning, bias testing, audit logs, and human-in-the-loop controls.
  • evaluation frameworks, accuracy benchmarks, hallucination monitoring, and output labelling pipelines.
  • Sprint delivery and production reliability.
  • Weekly quality scorecard.
  • Hiring, performance management, and coaching.
  • prompt tracing, hallucination detection, and output evaluation (Langfuse, Ragas, or equivalent).
  • model evaluation scores, accuracy/recall metrics, agent success rates, and DORA metrics for the team.
  • model versioning, A/B evaluation, and continuous prompt optimisation pipelines.

Requirements

  • 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.
  • document pipelines, chunking, embedding, vector databases, re-ranking, and similarity thresholds.
  • AWS Bedrock, SageMaker, or equivalent.
  • Track record shipping AI product features on schedule in a SaaS context — not just prototypes or internal tools.
  • prompt discipline, eval-driven development, responsible AI controls, and production-grade reliability.
  • EAM, ERP integrations (SAP, Maximo), maintenance workflows, or field operations.
  • tool calling, agent-to-agent delegation, and human-in-the-loop checkpoints.
  • token budgets, model tiering, and caching strategies.
  • MongoDB and change stream-based event architectures.
  • & Tools AI / ML AWS Bedrock, SageMaker, LiteLLM, LangGraph, Milvus (vector DB), AWS Neptune (knowledge graph), Langfuse Backend Node.js / TypeScript, Python, MongoDB Infrastructure AWS, Docker, GitLab CI/CD Observability Langfuse, Sentry, CloudWatch Tools GitLab, Jira, SonarQube Compensation & Growth Reports to VP PE&A. Path to Sr.

Preferred

  • Knowledge graph architectures (AWS Neptune, Neo4j) for grounding agent outputs in structured asset data.
  • image-based defect detection, document OCR, or form digitisation.

Benefits

  • Competitive compensation and equity tied to measurable impact on AI accuracy and performance.
  • A platform to shape the semantic intelligence layer of a category-defining industrial SaaS company.
  • Access to cutting-edge AI, data, and observability toolchains for continuous learning and innovation.

LLM governance

All inference within Innovapptive’s AWS VPC — no data to external LLM endpoints.

Tech Stack

EM or platform leadership as integration becomes a core horizontal capability.

What We Offer

Innovapptive does not accept and will not review unsolicited resumes from search firms . Innovapptive is an equal opportunity employer and is committed to a diverse and inclusive workplace. Qualified applicants will receive consideration for employment without regard to race, color, religion or creed, alienage or citizenship status, political affiliation, marital or partnership status, age, national origin, ancestry, physical or mental disability, medical condition, veteran status, gender, gender identity, pregnancy, childbirth (or related medical conditions), sex, sexual orientation, sexual and other reproductive health decisions, genetic disorder, genetic predisposition, carrier status, military status, familial status, or domestic violence victim status and any other basis protected under federal, state, or local laws