Job Description

About Krank:

We are a technology-driven SaaS company building products for the heavy equipment and industrial sector. Our ecosystem includes:

- Web-based marketplace and workflow platforms

- React Native mobile applications for iOS and Android

- Smart glasses–based workflows for remote inspections

- AWS-hosted backend services built using Node.js

We work at the intersection of engineering, product design, and modern cloud technologies to deliver efficient, reliable, and real-world solutions for industrial operations. Our culture is highly collaborative, product-focused, and centered on building meaningful tools that improve field workflows, inspections, and equipment management at scale.

About Inspeq:

Inspeq is a SaaS platform for inspections, asset management, and work orders. It’s designed for heavy equipment rental, mining, and construction companies, enabling them to manage operations more efficiently with modular, configurable workflows and AI-powered features like Remote Workorders (live remote guidance) and AI Discrepancy Reports.

We are looking for an AI Engineer to design, build, and deploy AI-powered features across our products. You will work on real-world applications of machine learning and generative AI, turning data and models into production-ready systems used by customers in operational and industrial environments.

This is a hands-on role sitting between engineering and product — less research, more building.

Key Responsibilities:

• Design and implement AI/ML solutions for real product use cases

• Build and integrate LLM-based features (chat, extraction, summarisation, classification, etc.)

• Work with APIs such as OpenAI, Anthropic, or open-source models

• Develop pipelines for data ingestion, preprocessing, and model inference

• Fine-tune and evaluate models where required

• Implement prompt engineering, RAG (Retrieval Augmented Generation), and embeddings

• Deploy models and AI services to production (AWS/Azure)

• Monitor performance, cost, latency, and quality of AI systems

• Collaborate with product, backend, and frontend teams to ship features end-to-end

Core Requirements:

• 3+ years experience in software engineering or ML/AI roles

• Strong engineering fundamentals (system design, APIs, data handling)

• Experience with ML frameworks

• Practical experience with LLMs and generative AI

• Experience working with structured and unstructured data

• Familiar with vector databases

• Comfortable deploying services in cloud environments

Nice to Have:

• Experience with Ai Application framworks

• Experience building AI features in SaaS products

• Knowledge of speech-to-text, vision models, or OCR

• Experience with MLOps (model versioning, monitoring, CI/CD)

• Understanding of security, privacy, and compliance for AI systems

• Experience building AI for operational, industrial, or enterprise workflows is a strong plus

What This Role Is (and Isn’t):

This role is:

• Product-focused

• Engineering-heavy

• About shipping AI into real systems

This role is not:

• Pure academic research

• Kaggle competitions

• Training giant models from scratch

Example Use Cases They’ll Work On:

• AI-powered data extraction from inspection reports, forms, images, or voice

• Intelligent assistants for field engineers and operations teams

• Automated reporting and asset insights

• Anomaly detection across inspections and work orders

• Workflow automation for industrial processes

Ideal Profile:

Someone who:

• Can actually build things, not just talk about models

• Understands trade-offs between accuracy, cost, and latency

• Is excited by messy real-world operational data

• Thinks in systems, not just notebooks

• Treats AI as a product capability, not a science experiment