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Protocol Intelligence
Data-driven signals on your job's competitivenessMetro location and reputable backers increase applicants, but niche LLM/agent expertise reduces candidate pool.
Highly domain-specific production LLM, RAG, and industrial SaaS experience required limits cross-industry fit.
Explicit 7+ years, 3+ years AI team management, and mandatory LLM/RAG tech create strict filters.
Job Description
Structured overview of role & requirementsAbout This Role
Lead and own the AI Engineering team responsible for the design, development, deployment, and reliability of product-facing AI agents for industrial operations.
Manage the end-to-end lifecycle of AI agents including architecture, prompt engineering, evaluation, and production monitoring with strong emphasis on RAG and knowledge infrastructure.
Accountable for sprint delivery, hiring, performance management, and maintaining agent quality metrics such as evaluation frameworks, hallucination monitoring, and accuracy benchmarks.
Minimum Requirements
7+ years software engineering experience with at least 3 years managing teams delivering AI/ML or LLM-powered enterprise production products.
Hands-on experience with LLM orchestration frameworks (e.g., LangGraph, LangChain) and RAG architecture including document pipelines, embeddings, vector databases.
Experience with managed inference infrastructure such as AWS Bedrock or SageMaker and AI observability tools (e.g., Langfuse, Ragas).
Work Experience Required: 7+ years in software engineering with 3+ years in AI team management. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Proven ability to ship AI product features on schedule in SaaS environments beyond prototypes or internal tools.
Data-driven approach focused on model evaluation scores, AI accuracy, agent success rates, and engineering metrics (DORA).
Experience managing teams with strong engineering standards around prompt engineering discipline, responsible AI, and production-grade reliability.