Senior ML/AI Engineer

RBC • Toronto, ON

1d • Onsite

About The Position

RBC Technology Infrastructure seeks a full stack AI Engineer to explore and operationalize big data sources to reduce outage and down time for RBC services that leads to improve user experience and save costs. Seeking AI Eng with experience in applied research and problem solving to join our team. The successful candidate will have experience with developing and deploying production grade AI/ML solutions, have broad expertise in statistics, analytics, ML and strong programming skill. Join our team at the forefront of technological advancement, where you'll develop applications such as autonomous AI agents that leverage and fine-tune large language models (LLMs) and other deep learning models. As an AI Engineer, you will play a pivotal role in building intelligent systems that transform the way we operate and deliver value.

Requirements

• 2+ years of industry experience required working on real-world problems
• Master or Ph.D. degree in an analytical field of study (e.g. Computer Science, Engineering, Mathematics, Statistics, or related quantitative field).
• Experienced with AI/ML infrastructure and model deployment for Gen AI applications in production environments and supporting enterprise-scale use cases
• Software Development Experience: Experience in software development with proficiency in Python, Java, or C++.
• Strong understanding of software engineering principles and practices.
• Machine Learning Expertise: Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.
• Knowledge of natural language processing techniques and LLMs.
• Model Development and Deployment: Experience in training, fine-tuning, and deploying machine learning models.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for model deployment.
• Data Handling Skills: Proficient in data preprocessing, feature engineering, and working with large datasets.
• Understanding of data privacy and security considerations.
• Strong foundation in ML and AI basics, knowledge of Inferencing, fine-tuning, model architectures, Embeddings.
• Hands-on experience implementing solutions using modern ML and Deep Learning frameworks, such as PyTorch, TensorFlow, Scikit-Learn, or Hugging Face Transformers
• Hands-on experience designing graph data models and working with graph databases (Neo4j, Amazon Neptune, TigerGraph) and/or knowledge graph frameworks (RDF/OWL, property graphs, SPARKQL)
• Familiar with software engineering industry best practices, including coding standards, testing methods, code reviews, and version control
• Experience working with technical and non-technical project stakeholders to scope, formulate, deploy, and maintain data science systems.
• Self-driven problem solver, able to adapt and thrive in a dynamic, ambiguous, and customer-faced environment.
• Excellent communication skills to articulate complex concepts to technical and non-technical stakeholders.
• Ability to prioritize work and manage multiple work streams concurrently.
• In-depth knowledge in machine learning and deep learning algorithms.
• Excellent working with structured and non-structured data.
• Excellent knowledge in Python, PySpark, SQL.
• Familiarity with GIT (GitHub)
• Experience with cloud-based data platforms such as Azure or AWS.
• Experience with data visualization tools such as Tableau, Looker, and Power BI.

Nice To Haves

• Experience architecting large scale ML systems.
• Experience working knowledge of Reinforcement learning (DynaQ/Q+, SARSA, TD, Monte Carlo).
• Knowledge in AIOps domain.
• Knowledge of IT Operation Monitoring Tools (Dynatrace, Moog, GEM, Pager Duty, etc )

Responsibilities

• Design and Develop AI Applications, Create and deploy autonomous AI agents utilizing LLMs and deep learning models.
• Fine-tune and customize pre-trained models (e.g., GPT-4, BERT, Llama, Qwen) for specific use cases.
• Implement Machine Learning Algorithms. Develop and optimize algorithms for natural language processing, computer vision, and other AI domains.
• Apply transfer learning and reinforcement learning techniques to enhance model performance.
• Collaborate Across Teams. Work closely with cross-functional teams to integrate AI solutions into existing products and services.
• Participate in code reviews, design discussions, and team meetings to foster a collaborative environment.
• Operational Excellence. Ensure the scalability, reliability, and security of AI models and systems.
• Monitor and analyze system performance, addressing any issues proactively.
• Continuous Learning and Innovation. Stay current with the latest advancements in AI and machine learning.
• Experiment with new technologies and methodologies to improve existing solutions.
• Mentorship and Knowledge Sharing. Provide guidance and training to team members on AI technologies and best practices.
• Document processes, design patterns, and technical implementations for future reference.
• Lead full life-cycle Data Science solutions from beginning to model deployment and monitoring and partner with the engineering team to ensure best practices for ML model deployment.
• Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.
• Experience in (Python, Apache Spark, PySpark, R, Scala, SQL, NoSQL, etc.) to obtain, integrate, manipulate, and analyze data from multiple sources.
• Expertise in statistical data analysis (e.g. univariate/bivariate analysis) and data quality assessment.
• Build Machine Learning, Deep Learning and statistical models to solve specific business problems.
• Developing predictive data models, anomaly detection model, quantitative analyses and visualization of targeted big data sources.
• Leading data exploration and analytic projects and providing on-going coaching of big data topics (visualization, data mining, analytic techniques).
• Exploring and implementing semantic data capabilities through NLP, text mining and machine learning techniques.
• Overseeing the acquisitions and ingestions of data from structured and unstructured sources, while ensuring quality and comprehensiveness of data.
• Utilizing APIs to collect data from various products into the Data Warehouse Database.

Benefits

• bonuses
• flexible benefits
• competitive compensation
• commissions
• stock where applicable
• Leaders who support your development through coaching and managing opportunities
• Ability to make a difference and lasting impact
• Work in a dynamic, collaborative, progressive, and high-performing team
• A world-class training program in financial services
• Opportunities to do challenging work
• Opportunities to take on progressively greater accountabilities
• Opportunities to building close relationships with clients
• Access to a variety of job opportunities across business and geographies.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior