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 glassesbased workflows for remote inspections
AWS-hosted backend services built using
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. Its 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.
Role Overview:
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 Isnt):
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 Theyll 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
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 platformsReact Native mobile applications for iOS and AndroidSmart glassesbased workflows for remote inspectionsAWS-hosted ba...
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 glassesbased workflows for remote inspections
AWS-hosted backend services built using
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. Its 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.
Role Overview:
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 Isnt):
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 Theyll 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
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