Senior AI Engineer

Scotiabank • Dallas, TX

4d

About The Position

Scotiabank is seeking an experienced Senior AI Engineer to design, develop, deploy, and scale AI-powered applications across the enterprise. The role combines software engineering with machine learning, Generative AI, agentic systems, cloud platforms, and MLOps. The engineer will work with data scientists, platform engineers, architects, product teams, and business stakeholders to bring AI solutions from concept to production while ensuring security, governance, scalability, and regulatory compliance.

The position contributes to Scotiabank’s AI platform and enterprise AI initiatives, building cloud-native services and integrating AI capabilities with enterprise data platforms and business applications. It also covers AI operations, data and knowledge engineering, governance and risk management, and collaboration with technical and business teams.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
• 5+ years of software engineering experience.
• 3+ years of AI/ML engineering experience in production environments.
• Experience deploying enterprise-scale AI solutions.
• Python programming experience.
• Knowledge of machine learning model development and deployment.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Knowledge of NLP and LLM technologies, fine-tuning and model optimization, and evaluation frameworks.
• Knowledge of Generative AI technologies, including OpenAI or Azure OpenAI, LangChain, LangGraph, Semantic Kernel, RAG architectures, vector databases, agentic AI frameworks, and prompt engineering.
• Knowledge of programming or data technologies including Java or Scala, SQL, and REST APIs.
• Knowledge of cloud and DevOps technologies including Docker, Kubernetes, Terraform, and GitHub Actions or Azure DevOps.
• Knowledge of data technologies including Databricks, Spark, Kafka, data lakes, and vector stores.
• Knowledge of MLOps technologies and practices including MLflow, model monitoring, CI/CD pipelines, feature stores, and experiment tracking.
• Strong problem-solving and analytical skills.
• Ability to translate complex AI concepts into business value.
• Excellent communication and stakeholder management skills.

Nice To Haves

• Experience working in highly regulated environments.
• Microsoft Azure experience; AWS or GCP experience is also relevant.
• Experience building enterprise AI agents and multi-agent systems.
• Experience with AI governance platforms.
• Knowledge of financial services, risk, compliance, AML, fraud, or customer analytics.
• Experience implementing Responsible AI frameworks.
• Microsoft Azure AI certifications or cloud certifications.
• Experience leading technical delivery teams.

Responsibilities

• Design and implement AI/ML and Generative AI solutions for business-critical use cases.
• Build intelligent applications using LLMs, RAG architectures, vector databases, and agentic workflows.
• Develop APIs, microservices, and reusable AI services for enterprise use.
• Implement prompt engineering, tool calling, function orchestration, and agent frameworks.
• Contribute to the design and evolution of Scotiabank’s AI platform.
• Build scalable AI services on cloud-native architectures and enable secure deployment of foundation models and AI agents.
• Integrate AI capabilities with enterprise data platforms and business applications.
• Develop CI/CD pipelines for machine learning and Generative AI workloads.
• Automate model deployment, monitoring, evaluation, and lifecycle management.
• Implement observability, telemetry, model performance tracking, and cost governance.
• Ensure AI solutions meet enterprise reliability and security standards.
• Build RAG pipelines using structured and unstructured data sources.
• Develop data ingestion, embedding, indexing, and retrieval frameworks.
• Work with large-scale data platforms, including batch and real-time processing systems.
• Collaborate with data governance teams to ensure responsible AI practices.
• Implement AI governance controls, guardrails, content filtering, and responsible AI requirements.
• Support regulatory and risk-management processes for AI systems.
• Conduct model validations, performance assessments, explainability, and compliance reviews.
• Partner with business leaders to identify impactful AI opportunities.
• Mentor junior engineers and promote engineering best practices.
• Collaborate with architecture, security, and infrastructure teams on enterprise initiatives.

Benefits

• Flexible benefit programs designed to support family, financial, physical, mental, and social health needs.

What This Job Offers

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