Lead Engineer, Generative AI and Machine Learning

Job Description

What is the opportunity?

This role offers a unique chance to pioneer the integration of Generative AI and machine learning (ML) into Site Reliability Engineering (SRE), driving transformative improvements in system reliability, efficiency, and scalability. You will work at the intersection of AI/ML innovation and cloud-native infrastructure, addressing critical challenges like anomaly detection, incident prediction, and automation. By leveraging cutting-edge technologies, you will empower organizations to minimize downtime, enhance observability, and optimize operational workflows, directly impacting business continuity and performance.

What will do you do?

• Design and deploy end-to-end AI/ML solutions to solve SRE challenges (e.g., log analysis, auto-remediation, and predictive maintenance).
• Develop models using supervised/unsupervised learning and Generative AI tools (e.g., LLMs, text-generation frameworks) to improve system resilience.
• Fine-tune models, engineer prompts, and integrate AI solutions with SRE tooling (monitoring systems, CI/CD pipelines).
• Collaborate with SRE, DevOps, and data science teams to scale solutions across cloud platforms (OCP, Azure).
• Translate AI insights into strategies for reducing downtime, automating tasks, and aligning with SRE principles (SLOs, error budgets).
• Build and maintain ML pipelines using Python, TensorFlow, PyTorch, and OpenAI APIs.
• Evaluate emerging AI technologies to advance reliability engineering practices.

What do you need to succeed?

• Technical Expertise: Strong experience in ML/Generative AI, Python, and frameworks like TensorFlow, PyTorch, or OpenAI APIs.
• SRE Knowledge: Familiarity with SRE concepts (SLOs, error budgets) and cloud-native environments (OCP, Azure).
• Problem-Solving Skills: Ability to address complex reliability challenges with AI-driven solutions.
• Collaboration: Effective teamwork with cross-functional teams (SRE, DevOps, data science).
• Innovation: Passion for exploring emerging AI technologies and advocating for novel approaches.
• Operational Focus: Commitment to ensuring scalable, production-ready deployments and optimizing model performance.

Must haves:

• Proven expertise in machine learning (ML) and Generative AI: Hands-on experience with frameworks like TensorFlow, PyTorch, or Hugging Face, and tools such as OpenAI APIs or LLMs.
• Strong programming skills in Python: Proficiency in developing and deploying ML models and pipelines.
• SRE/DevOps fundamentals: Familiarity with Site Reliability Engineering principles (e.g., SLOs, error budgets) and cloud-native infrastructure (OCP, Azure).
• Model deployment and scalability: Experience operationalizing ML models in production environments, including monitoring, maintenance, and optimization.
• Collaborative problem-solving: Ability to work with cross-functional teams (SRE, DevOps, data science) to translate technical insights into actionable solutions.
• Data analysis and engineering: Skills in preprocessing data, feature engineering, and working with large-scale datasets.

 Nice to haves:

• Prompt engineering and fine-tuning: Experience optimizing Generative AI models (e.g., LLMs) for domain-specific tasks.
• MLOps/AIOps tools: Familiarity with ML pipeline orchestration (e.g., Kubeflow, MLflow) and SRE tooling (e.g., Prometheus, Kubernetes).
• Anomaly detection/time-series analysis: Prior work in predictive maintenance, incident forecasting, or log analysis for infrastructure systems.
• Open-source contributions: Active participation in AI/ML or SRE-related open-source projects.
• Cloud certifications: Advanced credentials (e.g., AWS Machine Learning Specialty, Google Cloud AI Engineer).
• Domain knowledge in observability: Experience with tools like Grafana, ELK Stack, or Splunk for enhancing system visibility.

What's in it for you?

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and 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
• Flexible work/life balance options
• Opportunities to do challenging work
• Opportunities to take on progressively greater accountabilities
• Access to a variety of job opportunities across business and geographies

Job Skills

Agile Methodology, Group Problem Solving, IT Systems Integration, Organizational Leadership, Product Services, Software Development Life Cycle (SDLC), System Applications, System Integration Testing (SIT), Systems Software

Additional Job Details

Address:

RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

Application Deadline:

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Inclusion and Equal Opportunity Employment

At RBC, we believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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