Role Overview:

Innovapptives 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: 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, 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 Innovapptives 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.

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.