Digital Specialists

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 youll 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 youre 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 youre 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

Bachelors 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.

Build Nova Scotia is the Provinces Crown Corporation responsible for leading sustainable development of high-potential property and infrastructure across our province to drive economic and population short we build things-and how we build them matters as much as what we build. We engage Nova Scotias diverse communities to lead and support projects that create inclusive sustainable places that people love. We leverage our projects and programs as tools to bring people together in pursuit of keeping Nova Scotians healthy connected and thriving no matter where they live. A thriving community is a powerful thing and were doing our part to help communities reach their full potential by stewarding projects that enhance quality of life and community well-being build local pride and momentum and grow our economy.

Our goal is a diverse workforce that is representative at all job levels of the clients we serve. Build Nova Scotia has an Employment Equity Policy and we welcome applications from Indigenous People African Nova Scotians and other Racially Visible Persons Persons with Disabilities and Women in occupations or positions where they are under represented. If you are a member of one of these equity groups you are encouraged to self-identify on your application.