Agentic System Engineer – Intern

## About the role As an Agentic System Engineer Intern, you'll work closely with senior engineers to design, prototype, and operate components of a production-grade multi-agent AI platform used in real industrial environments—textile mills, automotive factories, and pharmaceutical plants. This is a hands-on systems internship, not a research-only role. You'll gain exposure to: - Agent orchestration - RAG pipelines - Deterministic validation - Observability & reliability engineering …aka the foundations of real-world, production AI systems. ## What You'll Work On ### Multi-Agent Platform Components You'll contribute (with mentorship) to components such as: - Retriever Agents – Vector search, metadata filters, knowledge graphs, time-series retrieval - Parser Agents – Datasheets, BOMs, CAD metadata, logs, and signal data - Synthesizer Agents – LLM/SLM orchestration with structured prompts and constraints - Validator Agents – Rule engines for electrical, thermal, and mechanical sanity checks - Planner Agents – Task planning for work orders, maintenance, and procurement flows - Memory Agents – Context storage, embedding compression, and session memory ## Key Responsibilities (Intern Scope) - Implement and test components of multi-agent workflows - Write clean, well-documented backend code - Integrate LLM APIs with rule-based systems - Participate in design reviews, debugging, and post-mortems - Support CI/CD pipelines, testing, and deployment - Learn production-grade AI engineering practices ## Required Skills & Background ### Core Requirements - Strong fundamentals in computer science or engineering - Proficiency in Python (Go is a plus) - Understanding of APIs, async programming, and backend services - Familiarity with data structures, algorithms, and basic system design ### AI & Systems Exposure (Any of the following) - Basic experience with LLMs, prompt engineering, or RAG concepts - Coursework or projects in distributed systems, AI, or data engineering - Exposure to databases (PostgreSQL, MongoDB, Redis, or similar) ## Why ENGINPILOT - Real Impact: Work on AI systems influencing real factories and assets - Serious Engineering: No hype—physics, validation, and correctness first - High Ownership: Interns ship real code used in production paths - Mentorship: Work directly with senior platform and systems engineers - Growth Path: Strong interns convert to full-time Agentic System Engineer role ## About the company ENGINPILOT is building an Engineering Intelligence Operating System that governs decisions across the full asset lifecycle—from design to decommissioning. We build constitutional, physics-grounded AI that respects immutable physical laws and operates safely in real industrial environments. ## Platform Ecosystem - ENGINPILOT.AI – Enterprise asset lifecycle intelligence with digital twin synchronization - ENGINgpt.AI – Physics-first conversational AI with multi-RAG and deterministic validation - ENGINPILOT.ARMY – Global engineering community driving bottom-up adoption