Senior AI Engineer

Scotiabank•Dallas, TX

10d•Onsite

About The Position

The Senior AI Engineer is a senior technical individual contributor responsible for designing, building, and operationalizing enterprise grade AI solutions in a highly regulated banking environment. This role provides deep technical leadership across AI engineering, MLOps/LLMOps, and governance by design, ensuring AI solutions are secure, scalable, auditable, and production ready. You will own complex AI systems end to end, influence platform standards, and act as a technical authority for AI delivery—bridging experimentation and enterprise production while meeting strict risk, privacy, and regulatory expectations.

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
8+ years of experience in cloud engineering, with 5+ years focused on AI/ML systems
Expert‑level proficiency in Python, SQL and cloud infrastructure
Hands‑on experience deploying AI solutions in cloud environments (Azure and GCP).
Deep understanding of production concerns: reliability, scalability, observability, cost, and security.
Experience delivering AI solutions in regulated industries (banking, financial services, insurance, healthcare).
Strong familiarity with model risk management, audit requirements, and regulatory review processes.
Hands‑on experience with enterprise MLOps / LLMOps tooling and platform design.
Experience designing platform‑level AI capabilities, not just individual models.

Responsibilities

Act as a technical lead for AI engineering initiatives, owning design decisions for complex, high‑impact AI solutions.
Define and contribute to reference architectures, reusable patterns, and “golden paths” for AI development and deployment across the bank.
Review and approve AI solution designs to ensure alignment with platform standards, security controls, and governance requirements.
Design and implement production‑grade AI services and pipelines (batch and real‑time) with strong focus on reliability, performance, and operational excellence in the cloud
Lead the packaging and deployment of models as scalable services (APIs, jobs, agents) with clear SLAs, monitoring, alerting, and runbooks.
Own complex problem resolution across environments, including production incidents related to AI systems.
Embed AI governance directly into engineering workflows, including: Security and access controls, Data classification and handling, Model risk management requirements, Privacy and consent controls, Responsible AI principles, Auditability and regulatory traceability
Partner closely with Risk, Compliance, Legal, and Architecture teams to ensure AI solutions meet internal and external regulatory expectations.
Lead implementation of Generative AI patterns such as Retrieval‑Augmented Generation (RAG), embeddings, semantic search, and agent workflows.
Ensure GenAI solutions are grounded in approved data sources, governed access, logging, and retention policies.
Define evaluation and monitoring approaches for GenAI outputs in regulated use cases.
Design and implement automated ML/LLM delivery pipelines covering training, evaluation, approval, deployment, and rollback.
Establish standards for model versioning, reproducibility, environment isolation, and controlled releases.
Reduce time‑to‑production while increasing safety, repeatability, and governance through automation.
Mentor senior and mid‑level engineers, raising the overall technical bar across AI engineering
Contribute to internal standards, documentation, and knowledge sharing.