Engineering Manager — AI Engineering
# Engineering Manager — AI Engineering **Innovapptive** · Hyderabad, Telangana · `On-site` 🕒 **Статус:** *Опубликовано: 8 дней назад* · *Источник: Indeed* --- ### About the Role Engineering 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. 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 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. 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 . CuJcygSypD
Наблюдалась 2026-09-30, впервые 2026-09-22, источник — Indeed.