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 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 Sullivans 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 $100MARR.

The Role

Innovapptive's Connected Worker Platform is entering its next phase of evolution. We are expanding our AI capabilities from foundational features into a robust portfolio of production-ready, purpose-built AI Agents designed specifically for industrial field operations.

These intelligent agents will revolutionize maintenance planning, work order automation, safety compliance, operator rounds, and knowledge assistance—all deeply grounded in enterprise asset data and Standard Operating Procedures (SOPs).

As the AI Engineering Manager, you will head the team responsible for designing, building, and operating this cutting-edge agent portfolio in production.

What You Own

AI Engineering team: 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, 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. Build the team to full operating capacity.

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.

You Will Be Measured On

Agent production reliability 99% uptime. Zero silent failures in retrieval or inference pipelines.

On-time feature delivery 90%. Regression rate

Agent accuracy: defined evaluation benchmarks met for each agent before GA release.

Customer adoption: agents in active use by 3 enterprise customers within first two quarters.

LLM governance coverage: every production AI feature has audit logs, human-in-the-loop controls, and bias test results on file.

Team build-out: AI Engineering operating at full with clear ownership and on-call rotations within 90 days.

Tech Stack Tools

AI/ML: AWS Bedrock, SageMaker, LiteLLM, LangGraph

Data/Graphs: Milvus (Vector DB), AWS Neptune (Knowledge Graph), MongoDB

Backend: Python, Node.js / TypeScript

DevOps/Infra: AWS, Docker, GitLab CI/CD

Observability: Langfuse, Sentry, CloudWatch

Tools: GitLab, Jira, SonarQube

Growth Reports to VP PEA. Path to Sr. EM or platform leadership as integration becomes a core horizontal capability.

What We Offer

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.

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 tor ace, 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