Principal Engineer, Cloud & AI

Geologic is a trusted data, software, and information solutions company committed to the Energy Industry. Every day we provide global customers with market-leading data, software, platforms, analytics, education, news, and insights that enable them to make vital decisions that drive growth and efficiency. Based in Calgary, with offices in London (UK) and Houston (US), we deliver critical data-driven intelligence ranging from surface and subsurface well and asset level information and insights to corporate performance benchmarking data and A&D transaction data.

People make us successful. We’re driven by a team of individuals whose unique backgrounds and experience allow us to provide the best products and customer service on the market. Together we’re transforming the energy intelligence landscape.

About the Role

Geologic is building a new cloud-first product, designed from the ground up on modern technology with AI at its core, and we’re looking for a Principal Engineer to be the technical lead who makes it real. This is our most senior hands-on engineering role: you’ll set the technical direction, write the code that matters most, and raise the bar for everyone building alongside you.

You’ll partner closely with the Software Development Manager, who owns the team, delivery, and roadmap, while you own the architecture, the hard technical calls, and the AI capabilities at the heart of the product. Together you’ll turn a demanding vision into something that ships and scales.

What You’ll Do

You’ll be the technical lead on our new cloud-first product, owning its architecture end to end and staying deeply hands-on in the codebase rather than stepping back into a purely advisory role. You’ll make the foundational decisions about how the product is built on AWS, from service architecture and data storage to authentication and security, and you’ll set the patterns the rest of the team builds on.

A central part of the job is AI. You’ll bring deep, practical experience with AI and models into the product, choosing the right approaches, building real features on top of them, and evaluating what actually works in production rather than in a demo. Just as important, you’ll roll that capability out to the wider team: sharing patterns, setting guardrails, and levelling up other engineers so AI becomes something the whole team can build with confidently, not a black box only you understand.

You’ll also be a force multiplier for quality. You’ll review the designs and code that carry the most risk, mentor engineers, and know when a problem needs your hands directly versus when it’s an opportunity to grow someone else. When the timeline is demanding, your judgment about what to build, what to simplify, and what to defer is what keeps the product both fast-moving and sound.

Qualifications

What We’re Looking For

• A senior, deeply hands-on engineer who still lives in the code and leads by building
• Deep, hands-on expertise with cloud platforms, especially AWS
• Strong practical experience with AWS architecture : services such as S3 and other storage, authentication and identity, networking, security, and cost-aware design
• Deep hands-on experience with AI and modern models , including building real product features on top of them
• A track record of rolling AI capabilities out to engineering teams , establishing patterns, guardrails, and best practices
• Experience acting as technical lead on a product or major system , ideally a cloud-first, greenfield build
• Fluency with modern, latest-generation tech stacks and a strong sense for what strong code and sound architecture look like
• The judgment to balance speed and quality under demanding timelines, and to mentor and elevate the engineers around you

Nice to Have

• Hands-on experience with specific AWS technologies such as Lambda, ECS/EKS, API Gateway, DynamoDB, RDS, Cognito, CloudFormation/CDK, and IAM
• Experience working with AI models (LLMs and foundation models), including AWS AI/ML services such as Bedrock and SageMaker, RAG pipelines, and model evaluation
• Background in the energy sector or with geospatial, subsurface, or large-scale data platforms
• Experience taking a greenfield product from zero to launch and scaling it in production