AI Engineer

at  BluWaveai

AI ENGINEER

BluWave-ai is dedicated to revolutionizing the renewable energy sector through the implementation of cutting-edge AI solutions. Our mission is to accelerate the adoption of clean energy in smart grids and facilitate the transition towards electrification of transportation.
We are seeking individuals who are passionate about driving change and making a positive environmental impact. Join us at the forefront of this exciting journey, where entrepreneurial spirits are encouraged, career growth is nurtured, and opportunities to shape a sustainable future abound.

Who you are

• A seasoned AI Engineer strongly motivated by building impactful and dependable products based on pragmatic and rigorous application of ML techniques.
• You have the drive to learn, evaluate, and apply a range of data science and ML techniques. The applications are real-time smart grid control and optimization solutions in the context of best scalability, availability, and security principles.
• You are a pragmatic innovator who thrives in a fast-paced, disciplined, and team-oriented environment where we strive individually while supporting, learning from, and building on each other’s ideas and efforts to succeed as a team.

REQUIREMENTS

• Education: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field (Ph.D. is a plus).
• 3 years of experience in data science or a related field, with a focus on time series forecasting and optimization.
• Time Series Forecasting: Strong knowledge and hands-on experience in developing time series forecasting models using statistical and machine learning approaches.
• Programming Skills: Proficiency in Python for data manipulation, analysis, and model development.
• Analytical Skills: Ability to apply mathematical concepts and statistical techniques to analyze complex time-dependent data and derive actionable insights.
• Data Visualization: Proficiency in data visualization and dashboarding tools like Grafana, Plotly, or similar.
• Communication: Excellent verbal and written communication skills for presenting complex concepts and findings.
• Teamwork: Proven ability to collaborate effectively in cross-functional teams.
• Continuous Learning: Strong desire to stay updated with advancements in data science, time series forecasting, and optimization.

Considered an asset:

• Familiarity with control and optimization of modern power and energy systems.
• Experience with optimization techniques such as linear programming, integer programming, and related tools (e.g. Pyomo, Gurobi, CPLEX).
• Deployment and Monitoring: Understanding of machine learning model deployment strategies and monitoring techniques.
• MLOps Tools: Familiarity with Kubeflow, MLflow, or similar for managing and deploying machine learning models.
• Cloud Platforms: Experience with Azure, AWS, or Google Cloud Platform and their machine learning services.
• Big Data Technologies: Knowledge of Apache Hadoop, Spark, or Hive and experience with large-scale time series datasets.
• Time Series Databases: Familiarity with InfluxDB, Prometheus, or TimescaleDB for efficient time series data storage.
• Version Control Systems: Experience with Git or similar tools for collaborative development.

General Information
Level: All experience ranges are encouraged to apply
Position Type: Full-time
Location: Ottawa, ON / Summerside, PE / Calgary, AB (hybrid, no remote)
Position Reports to: Software Engineering Manager
Diversity makes us stronger. BluWave-ai provides equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, gender, national origin, disability, or any other characteristic protected by applicable laws, regulations, or ordinances. Authorization to work in Canada is required for this position.
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• Time Series Forecasting: Develop and implement advanced time series forecasting models to predict future trends, demand, and other relevant variables. Apply techniques such as ARIMA, SARIMA, exponential smoothing, or machine learning algorithms tailored to time series data.
• Data Preprocessing and Cleaning: Clean, transform, and preprocess time series data to ensure data integrity and quality. Handle missing data, outliers, and other data anomalies appropriately.
• Feature Engineering: Identify and engineer relevant features to improve the accuracy and performance of forecasting models. Incorporate domain knowledge to enhance feature selection and extraction.
• Model Development and Evaluation: Build, train, and evaluate forecasting models using appropriate evaluation metrics. Select and fine-tune models to achieve optimal performance.
• Performance Monitoring: Continuously monitor and validate the accuracy and performance of forecasting models over time. Identify and address issues related to model drift or degradation.
• Collaboration and Communication: Collaborate with cross-functional teams, including stakeholders from different departments, to understand business requirements and provide actionable insights based on time series analysis and optimization.
• Visualization and Reporting: Create clear and compelling visualizations, reports, and dashboards to effectively communicate forecasting results, optimization recommendations, and key insights to both technical and non-technical stakeholders.
• Research and Innovation: Stay updated with the latest advancements in time series forecasting, optimization techniques, and related domains. Explore and propose innovative approaches to improve forecasting accuracy and optimization outcomes.