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, 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 orinference pipelines. ● On-time feature delivery ≥90%. Regression rate ● Agent accuracy: defined evaluation benchmarks met for each agent before GArelease. ● Customer adoption: agents in active use by ≥3 enterprise customers within firsttwo 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 6–8 HC with clear ownership and on-call rotations within 90 days. Tech Stack 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, Cloud Watch Tools GitLab, Jira, Sonar Qube Compensation 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 inclusiveworkplace. Qualified applicants will receive consideration for employment without regard torace, color, religion or creed, alienage or citizenship status, political affiliation, marital orpartnership 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, geneticpredisposition, carrier status, military status, familial status, or domestic violence victim statusand any other basis protected under federal, state, or local laws