AI Engineer – Reinforcement Agent / BIM Geometry Automation

Location

India / Remote / Hybrid

About the Role

We are building an AI-powered reinforcement agent that can generate, validate, and edit reinforcement for structural elements such as walls, slabs, beams, columns, and precast components.

This is not a generic chatbot or RAG role. The work sits at the intersection of:

• AI agents and structured reasoning

• Computational geometry and 3D/2D shape handling

• BIM / IFC workflows

• Rule-based engineering automation

• Reinforcement detailing logic

We are looking for an engineer who can help design and build a production-grade system that understands element geometry, engineering intent, detailing rules, and output constraints, and converts them into reinforcement layouts, shape definitions, and downstream deliverables such as BBS, IFC-ready outputs, and visual review workflows.

What You Will Work On

• Build an AI-assisted reinforcement generation engine for structural concrete elements.

• Convert geometry inputs into reinforcement intent, bar layouts, and deterministic outputs.

• Design systems that combine LLMs, rule engines, and geometric algorithms rather than relying only on prompting.

• Work with IFC/BIM data, element geometry, local coordinate systems, openings, drilling zones, covers, hooks, laps, bend radii, and edge conditions.

• Develop logic for reinforcement placement across walls, slabs, beams, columns, and precast elements .

• Build validation workflows to check engineering constraints, collisions, spacing, cover, continuity, and constructability.

• Generate structured outputs for downstream rendering, editing, and BBS workflows.

• Support visual review flows where users can inspect, edit, and approve generated reinforcement .

• Improve the agent’s reliability on edge cases through evals, test datasets, deterministic checks, and debugging tools .

• Collaborate closely with product, structural engineering, and frontend/viewer teams .

Key Responsibilities

• Design backend services and workflows for reinforcement-agent execution.

• Build geometry-aware processing pipelines from IFC / extracted element data .





• Implement reinforcement placement logic using structured engineering rules.

• Create schemas for engineering intent, bar sets, bar positioning, and output validation .

• Integrate LLM/agent components where they genuinely add value, such as reasoning over engineering context, exception handling, or workflow orchestration.

• Ensure outputs are deterministic, traceable, and debuggable .

• Build or integrate tools for bar visualization, review, and correction.

• Write tests for critical edge cases including openings, corners, peripheral bars, lifters, face mesh interaction, and shape generation.

• Contribute to production deployment, monitoring, and performance optimization .

Must-Have Skills

• Strong Python experience.

• Strong backend engineering experience with APIs and production systems .

• Experience building AI/agent systems using structured outputs, tool use, workflow orchestration, or LangGraph-like systems.

• Good understanding of computational geometry, coordinate systems, transformations, and rule-based logic .

• Experience handling structured engineering or CAD/BIM-like data.

• Ability to break complex domain logic into deterministic, testable modules .

• Comfort working with LLMs as one component in a larger system rather than as the entire solution.

• Strong debugging and systems thinking .

Strongly Preferred

• Experience with IFC, BIM, IfcOpenShell, CAD/CAM, Revit, Allplan, Tekla, or similar ecosystems.

• Experience with reinforcement detailing, rebar modeling, BBS generation, or structural engineering workflows .

• Understanding of concepts such as cover, spacing, hooks, bend radius, lap lengths, anchorage, edge bars, opening reinforcement, mesh placement, and constructability.

• Experience with 3D viewers or geometry visualization pipelines.





• Experience with OpenCV / OCR / drawing extraction for engineering drawings.

• Experience building review tooling, QC pipelines, or human-in-the-loop engineering systems.

Nice to Have

• Structural or civil engineering exposure.

• Experience with Three.js, WebGL, or browser-based model viewers.

• Experience with optimization and search in geometric layouts.

• Familiarity with shape-code systems , reinforcement catalogs, and regional detailing standards.

• Experience creating evaluation datasets and benchmark workflows for engineering AI systems .

What Success Looks Like

In this role, success means you can help us move from “interesting prototype” to a robust system that:

• Understands element geometry correctly.

• Places reinforcement using explicit engineering logic.

• Handles real-world edge cases.

• Produces structured, editable outputs.

• Supports visual review and correction.

• Improves reliably over time through tests, evals, and production feedback.

Who Will Be a Good Fit

You are likely a strong fit if you are one of these:

• An AI engineer with robust geometry/CAD/BIM instincts.

• A computational geometry or graphics engineer interested in engineering automation.

• A backend AI engineer who has worked on structured reasoning systems and can learn reinforcement logic fast.

• A technically strong engineer with exposure to structural detailing or BIM workflows.

Who Will Not Be a Good Fit

This role is likely not a fit for someone whose experience is limited to:

• Generic RAG/chatbot projects only.

• Prompt engineering without backend/system design.

• LLM demos without production ownership.

• AI search/retrieval roles with no geometry, CAD , or engineering logic exposure.

Tech Stack Context

Our work may involve parts of the following stack depending on the module:

• Python

• FastAPI

• LLM APIs / agent frameworks

• IFC / IfcOpenShell / geometry processing

• Postgres / structured schemas

• OpenCV / extraction pipelines

• Three.js or viewer integrations

• AWS / production cloud workflows

📌 AI Engineer –BIM Geometry Automation (India)
🏢 Nexifyr Consulting
📍 India