Actlabs is a global leader in laboratory analytical services with 35+ years of experience across mining, geochemistry, environmental testing, agriculture, cannabis, health, life sciences, and petroleum industries. We are building a dedicated AI and Data Science team to unlock the value of our extensive data asset and drive competitive intelligence, operational efficiency, and client value. We are investing in the future, implementing innovative strategies to maintain our leadership and strengthen our impact across the sectors we serve. This is an exciting opportunity for dynamic individuals who want to be part of an established and rapidly growing company.

Role Overview: The AI/ML Engineer owns the build layer of our AI initiatives within an agile, cross-functional team alongside Data Engineers, Data Scientists, a Project Manager, and Data Analysts. You will be responsible for model architecture, data pipelines, production deployment, MLOps infrastructure, and system integration taking AI initiatives from experimentation to production impact..

Education Required: Master's or PhD in Statistics, Mathematics, Data Science, Computer Science, Physics, or a related quantitative field strongly preferred; Bachelor's considered with exceptional applied experience

Experience Required: • 5+ years of hands-on data science experience, including 3+ years of building and deploying machine learning models in production environments • Demonstrated experience taking ML systems from prototype to deployed, monitored production services

Preferred Qualifications Experience deploying Large Language Models in on-premises or cloud environments Familiarity with RAG architectures, vector databases, and embedding-based search Hands-on experience with GCP or Azure for deploying and managing AI workloads Experience in regulated or accredited environments (ISO, GMP, or equivalent) Familiarity with geochemistry, mining, or environmental science domains

Other Requirements: • Expert Python: pandas, NumPy, scikit-learn, statsmodels, and visualization libraries • Deep grounding in inferential statistics, hypothesis testing, regression, causal inference, and experimental design • Hands-on experience with model versioning, experiment tracking (MLflow or equivalent), CI/CD for ML, and model monitoring • Strong fundamentals in Git, testing, modular design, code review, and Docker containerization • Strong understanding of temporal validation and evaluation metrics for real-world datasets

Responsibilities: Applied Machine Learning Development Design, develop, train, and evaluate machine learning models for production use cases, translating business and scientific requirements into well-defined ML problem formulations with appropriate architectures, metrics, and acceptance criteria. Perform feature engineering on structured and unstructured data, building reusable transformation logic for training and inference. Balance model complexity, interpretability, and operational constraints, running controlled, reproducible experiments to validate improvements. MLOps & Model Lifecycle Management Build and maintain a full MLOps stack: model versioning, experiment tracking, CI/CD pipelines, automated retraining triggers, and rollback processes. Implement monitoring for performance drift, data drift, and production failure modes, with defined alerting and response procedures that meet quality assurance and accreditation requirements. Apply responsible AI best practices including bias detection, confidence scoring, and audit logging for production model decisions. Data Pipelines & System Integration Design and build scalable, production-grade data pipelines for training, batch inference, and real-time inference — observable, testable, versioned, and recoverable from failure. Build and maintain APIs that expose model inference as low-latency, high-availability services, and integrate AI features into internal reporting and business intelligence platforms. Ensure all production AI systems meet standards for reliability, observability, security, and auditability. Collaboration & Cross-Functional Work Partner with the Data Scientist on feature engineering, model validation, and the handoff from experimentation to production, and with the Data Engineer on data architecture and infrastructure planning. Work with the AI Projects Manager to scope requirements and communicate engineering tradeoffs, and engage domain experts to understand what makes AI outputs credible and usable.

Our Hiring Process We keep our process straightforward and respectful of your time. Shortlisted candidates will move through the following stages: Introductory Alignment Assessment: Shortlisted candidates will be asked to complete a brief written questionnaire prior to any live conversation, giving us an early sense of your thinking and approach.

Take-Home Assignment: Candidates will complete a practical, work-relevant assignment designed to assess problem-solving, analytical structure, and the ability to communicate findings clearly.

Technical Interview (Online/In-Person): A live problem-solving coding session followed by a panel interview. Candidates are allowed to use AI tools, we evaluate how you think and work, not whether you have memorized syntax.

Team Interview (In-Person): A structured conversation with the broader team assessing your subject knowledge, collaboration style, communication, and alignment with our ways of working.

A Workplace for All: At Actlabs, we celebrate diversity and are committed to creating an inclusive workplace environment for all employees. We are proud to be an equal opportunity employer, and we strive to ensure that all voices are heard, all cultures are respected, and a variety of perspectives are not only welcome but essential to our success. We treat each other with fairness and dignity, regardless of race, gender, nationality, ethnic origin, religion, age, sexual orientation, or any other personal consideration. We believe that a diverse and inclusive workforce is key to driving innovation and achieving excellence.