Digital Specialists
Build Nova Scotia - 3 Jobs
Halifax, NS
Job Details:
Benefits:
As a Digital Solution Specialist in each of the identified roles below, you will play an important role in advancing digital transformation and infrastructure priorities. You will work alongside multi-disciplinary, high-impact technical teams, providing expert guidance on applying human-centred design, modern data strategies, and innovative deployment approaches to deliver transformative digital solutions. You will serve as a recognized technical authority, overseeing the design, development, maintenance, and measurement of digital infrastructure, products, and services.
These are two (2) year term positions.
Please indicate in your cover letter your preference for each position outlined below.
1. Cloud Infrastructure Engineer(s): where you will design and manage scalable, secure cloud environments for digital products and artificial intelligence workloads. You will understand how to provision cloud resources and optimize costs, all while ensuring infrastructure meets compliance and security standards.
2. Data Engineer(s): where you will manage pipelines to collect, clean and transform data. You will work with teams who manage structured and unstructured data stores (e.g. data lakes and warehouses), while ensuring data is versioned, reliable, and ready to be ingested for AI models.
3. Algorithm Specialist(s): where you will prototype new approaches and run experiments to test solution feasibility. You will experiment with different AI model architectures (e.g. LLMs, NLP, CV, RL), while staying abreast of emerging ML/AI research, translating research into practical delivery.
4. ML Engineer(s): where you'll help build, train, and fine-tune AI models. You will optimize models for performance, reduced latency, and scalability. You understand how to control model versions, manage distributed model training, and deploy your models in cloud environments.
5. MLOps Engineer(s): where you're responsible for product and AI model lifecycle, including deployment, monitoring, and rollback. You can maintain CI/CI pipelines for ML workflows, monitor models for drift, bias, and performance degradation. You manage dashboards, making predictions for model drift while also being skilled at building automated model retraining pipelines when new data is available.
6. Security Engineer: where you're responsible for ensuring the security, compliance, and resiliency of AI systems and infrastructure. You can assess and mitigate risks across data pipelines, models, and deployment environments. You monitor for vulnerabilities, adversarial attacks, and data leakage while implementing security best practices and governance controls. You manage compliance with regulatory frameworks and are skilled at embedding security-by-design into AI workflows, ensuring trustworthy and reliable AI solutions.
Education
Bachelor's degree in information technology and a minimum of three (3) years of related experience. OR
Graduation from a recognized Information Technology program and a minimum of three and a half (3.5) years of related experience. OR
An equivalent combination of education, training, and experience.
Technical Skills (may differ depending on specialist role):
1. Cloud Infrastructure Engineering
· Strong experience with cloud platforms (AWS, Azure, GCP).
· Expertise in container orchestration (Kubernetes, Docker).
· Knowledge of networking, identity and access management, and cloud security best practices.
· Experience provisioning and managing GPU/TPU resources.
· Skills in infrastructure cost optimization (e.g., right-sizing instances, monitoring usage).
· Proficiency with infrastructure as code / automation tools (Terraform, GitOps).
2. Data Engineering
· Proficiency in SQL, Spark, or similar ETL frameworks.
· Strong knowledge of data modeling, governance, versioning, lineage, and quality assurance.
· Experience maintaining metadata catalogs, data lakes, warehouses, and feature stores.
· Ability to build and optimize data ingestion and transformation pipelines (ETL/ELT).
3. Machine Learning & AI
· Advanced knowledge of ML/AI techniques (transformers, CNNs, RNNs, reinforcement learning).
· Strong programming skills in Python with ML frameworks (PyTorch, TensorFlow, JAX).
· Experience with distributed training, model fine-tuning, and hyperparameter optimization.
· Familiarity with optimization techniques (quantization, pruning, parallel/distributed training).
· Ability to evaluate and translate research papers into experiments and practical applications.
4. Application & Integration
· Full-stack or backend engineering skills (APIs, microservices, web frameworks like FastAPI, Flask, Node).
· Experience deploying models as services (REST, gRPC).
· Ability to integrate authentication, monitoring, and logging into applications.
· Experience building front-end/UI for AI applications and dashboards.
5. MLOps & Production
· Proficiency with CI/CD pipelines for ML workflows.
· Experience with ML deployment frameworks (Kubeflow, MLflow, Seldon).
· Familiarity with logging, monitoring, and observability tools (Prometheus, Grafana, ELK).
· Skills in monitoring and managing model drift, bias, and performance degradation.
· Ability to design and maintain automated retraining pipelines.
Competencies
Excellent communication skills (written and verbal).
Ability to work independently and collaboratively in cross-functional teams.
Strong attention to detail and adaptability to changing priorities.
Effective time management and ability to manage multiple priorities under tight deadlines.
Comfortable working remotely using Microsoft 365, Teams, and other collaboration tools.
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