Lead Developer

Dahrouge Geological Consulting Ltd.•Edmonton, AB

3d•Onsite

About The Position

The Lead Developer leads the design, development, and delivery of Dahrouge Group’s software and machine-learning products. This is a hands-on technical leadership role: setting the engineering standard, leading a small development team, and taking products from concept to production. The role champions AI-leveraged development as the default way of working, driving exceptional delivery velocity from a lean team. Deep fluency with modern AI and machine-learning tooling is essential, and an interest in the natural resources sector is a strong asset.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
6+ years of software development experience, including demonstrated experience leading or mentoring engineers.
A strong track record of shipping and maintaining production-grade software.
Demonstrated ability to deliver at high velocity using AI-assisted development.
Deep proficiency in Python and modern software engineering practices.
Hands-on experience with the machine-learning stack (e.g., PyTorch, Lightning, Hugging Face) and taking models to production.
Demonstrated mastery of AI-assisted, agentic development tools and the ability to use them to multiply team output.
Strong experience with cloud platforms, containerization (Docker), version control (Git), and CI/CD.
Strong system design, analytical, and problem-solving abilities.
Sound product and design judgment, with a high bar for software craft.
Excellent written and verbal communication skills, including the ability to present technical concepts to non-technical audiences.

Nice To Haves

Coursework or a degree in Geology, Geophysics, Earth Sciences, or a quantitative discipline is considered an asset.
Experience with machine-learning systems in production is an asset.
Experience in the mining, natural resources, or geoscience sector is an asset.
Certifications in cloud platforms (e.g., Microsoft Azure, AWS, or Google Cloud) or machine-learning specializations are considered an asset.
Fluency or genuine interest in geoscience, mining, or natural resources, and familiarity with spatial or geological data (GIS, drillhole, geochemistry).
Strong front-end development experience (React or similar) and a strong design sensibility.
Experience building or scaling a small engineering team from an early stage.
Familiarity with geoscience software such as Leapfrog or Vulcan.

Responsibilities

Lead the architecture, design, and delivery of the company’s software product suite, spanning customer-facing SaaS applications and machine-learning systems.
Define coding standards, architectural patterns, and the overall quality bar.
Conduct and oversee code reviews, and contribute directly to the codebase as a hands-on technical leader.
Take machine-learning models to production in partnership with the company’s AI research function, owning evaluation, fine-tuning, deployment, and monitoring.
Bring applied AI capabilities into customer-facing products.
Maintain the standards for reproducible, production-grade ML.
Design and maintain automated pipelines to acquire, clean, and transform geological and operational data for use in products and models.
Ensure data quality, accuracy, and integrity from source through to product.
Champion AI-Native Development & Delivery’s default way of working.
Build the internal tooling, workflows, and agentic harnesses that let a small team out-deliver much larger ones.
Establish and maintain best practices in DevOps, CI/CD, testing, and documentation.
Set delivery cadence and priorities without unnecessary process overhead.
Lead, mentor, and grow the development team, setting a high standard for craft and ownership.
Work closely with leadership, geologists, and operations staff to translate strategy into a clear product roadmap.
Communicate technical concepts clearly to non-technical stakeholders.
Ensure products and systems meet appropriate security standards and data governance practices, including tenant isolation and protection of client and proprietary data.
Participate in security reviews and support adherence to applicable regulatory and compliance requirements.