Senior Product Manager — AI-native construction intelligence Remote / Hybrid · →

Meramia Technologies; Dubai; Remote; AED 240,000 - 360,000; Full time

Job summary

Meramia Technologies is seeking an experienced AI/ML product leader to bridge engineering of AI harnesses with on-site teams like project managers and field engineers. You will define harness workflows, integrate LLM/vision models with project records, and stand up agents with evaluators to ensure accuracy and safety on live sites.

You will visit construction sites, gather input from managers and subcontractors, and articulate bottlenecks before issuing tickets.

Qualifications

• Three or more years running AI or ML products: agent architectures, retrieval, computer vision, and an eval harness you have actually used.
• The construction lifecycle — pre-construction, bid, schedule, execute, handover — and the tools the trades already live in: Procore, Autodesk Construction Cloud, Primavera P6, Revit, OpenSpace.
• A plan for unstructured site data: photographs, 360 scans, drone footage, PDF specs, BIM metadata.
• Four or more years of product work in B2B software, PropTech, ConTech, or developer infrastructure.
• Interviews, prototypes, a backlog you will defend, and the ability to talk to researchers, engineers, designers and a superintendent in the same week.

Responsibilities

• Set the sequence for field-capture processing, plan parsing, submittal review, cost, progress and identify the current harness problem.
• Specify developer-facing and internal harnesses that bind LLMs and vision-language models to the project record to prevent hallucinations.
• Stand up agents that can flag hazards, follow deliveries, check builds, or draft change orders with evals attached.
• Go to site and talk to project managers, superintendents and subcontractors, documenting bottlenecks before writing tickets.
• Own the evals for accuracy, safety and reliability where outputs affect money or safety.
• Collaborate with sales and support to measure hours saved and risk reduction, not just claim it.

Skills

AI/ML product management

Agent architectures

Retrieval

Computer vision

Eval harness

Education

Civil engineering background

Tools

Procore

Autodesk Construction Cloud

Primavera P6

Revit

OpenSpace

Job description

The role sits between the engineering of AI harnesses — pipelines, agent orchestration, multi-model evals, systems grounded in the project record — and the people who run a site: general contractors, project managers, field engineers.

• Set the sequence: field-capture processing, plan parsing, submittal review, cost, progress — and say which of those is a harness problem this quarter.
• Specify developer-facing and internal harnesses that bind LLMs and vision-language models to the project record, so a hallucination is a test failure rather than a site surprise.
• Stand up agents that can flag a hazard, follow a delivery, check a build against the drawing, or draft a change order — always with an eval attached.
• Go to site. Talk to project managers, superintendents and subcontractors. Write the bottleneck down before you write the ticket.
• Own the evals for accuracy, safety and reliability wherever the output can move money or injure someone.
• Work with the people who sell and support the work so hours saved, risk avoided and rework cut are measured, not asserted.

01

What you will do

• Set the sequence: field-capture processing, plan parsing, submittal review, cost, progress — and say which of those is a harness problem this quarter.
• Specify developer-facing and internal harnesses that bind LLMs and vision-language models to the project record, so a hallucination is a test failure rather than a site surprise.
• Stand up agents that can flag a hazard, follow a delivery, check a build against the drawing, or draft a change order — always with an eval attached.
• Go to site. Talk to project managers, superintendents and subcontractors. Write the bottleneck down before you write the ticket.
• Own the evals for accuracy, safety and reliability wherever the output can move money or injure someone.
• Work with the people who sell and support the work so hours saved, risk avoided and rework cut are measured, not asserted.

What you bring

• Three or more years running AI or ML products: agent architectures, retrieval, computer vision, and an eval harness you have actually used.
• The construction lifecycle — pre-construction, bid, schedule, execute, handover — and the tools the trades already live in: Procore, Autodesk Construction Cloud, Primavera P6, Revit, OpenSpace.
• A plan for unstructured site data: photographs, 360 scans, drone footage, PDF specs, BIM metadata.
• Four or more years of product work in B2B software, PropTech, ConTech, or developer infrastructure.
• Interviews, prototypes, a backlog you will defend, and the ability to talk to researchers, engineers, designers and a superintendent in the same week.

02

What you bring

• Three or more years running AI or ML products: agent architectures, retrieval, computer vision, and an eval harness you have actually used.
• The construction lifecycle — pre-construction, bid, schedule, execute, handover — and the tools the trades already live in: Procore, Autodesk Construction Cloud, Primavera P6, Revit, OpenSpace.
• A plan for unstructured site data: photographs, 360 scans, drone footage, PDF specs, BIM metadata.
• Four or more years of product work in B2B software, PropTech, ConTech, or developer infrastructure.
• Interviews, prototypes, a backlog you will defend, and the ability to talk to researchers, engineers, designers and a superintendent in the same week.

Useful, not required

• Civil engineering, construction management, architecture, or computer science as a first training.
• Prompting and model-eval tools (LangSmith, MLflow, a custom CLI) used as instruments, not as theatre.
• A zero-to-one AI product shipped into a trade that still runs on paper and a WhatsApp group.

03

Useful, not required

• Civil engineering, construction management, architecture, or computer science as a first training.
• Prompting and model-eval tools (LangSmith, MLflow, a custom CLI) used as instruments, not as theatre.
• A zero-to-one AI product shipped into a trade that still runs on paper and a WhatsApp group.

The first quarter

• Run discovery on live accounts and audit the current models and their evals.
• Set the twelve-month map: which agentic workflows matter, and which harness gaps block them.
• Ship one feature — a plan-set delta check or live risk alert — with retention and accuracy defined before release.