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About This Role
Requisition ID: 276670
Salary Range: -
Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.
Purpose
We are seeking an experienced Senior AI Engineer to design, develop, deploy, and scale AI-powered applications across the enterprise. The ideal candidate combines strong software engineering expertise with deep knowledge of machine learning, Generative AI, agentic systems, cloud platforms, and MLOps practices.
You will work closely 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.
What You'll Do
AI Solution Development
- Design and implement AI/ML and Generative AI solutions for business-critical use cases.
- Build intelligent applications leveraging LLMs, RAG architectures, vector databases, and agentic workflows.
- Develop APIs, microservices, and reusable AI services for enterprise consumption.
- Implement prompt engineering, tool calling, function orchestration, and agent frameworks.
Enterprise AI Platform Engineering
- Contribute to the design and evolution of Scotiabank's AI platform.
- Build scalable AI services on cloud-native architectures.
- Enable secure deployment of foundation models and AI agents.
- Integrate AI capabilities with enterprise data platforms and business applications.
MLOps & AI Operations
- 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.
Data & Knowledge Engineering
- 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.
AI Governance & Risk Management
- 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.
Stakeholder Collaboration
- 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.
What You'll Bring
Education
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field.
Experience
- 5+ years of software engineering experience.
- 3+ years of AI/ML engineering experience in production environments.
- Experience deploying enterprise-scale AI solutions.
- Experience working in highly regulated environments is preferred.
Technical Skills
Programming
- Python (required)
- Java or Scala
- SQL
- REST APIs
AI & Machine Learning
- Machine Learning model development and deployment
- Deep Learning frameworks (PyTorch, TensorFlow)
- NLP and LLM technologies
- Fine-tuning and model optimization
- Evaluation frameworks
Generative AI
- OpenAI / Azure OpenAI
- LangChain, LangGraph, Semantic Kernel
- RAG architectures
- Vector databases
- Agentic AI frameworks
- Prompt engineering
Cloud & DevOps
- Microsoft Azure (preferred)
- AWS or GCP experience
- Docker
- Kubernetes
- Terraform
- GitHub Actions / Azure DevOps
Data Technologies
- Databricks
- Spark
- Kafka
- Data Lakes
- Vector Stores (Pinecone, Weaviate, Milvus, Cosmos DB)
MLOps
- MLflow
- Model monitoring
- CI/CD pipelines
- Feature stores
- Experiment tracking
Preferred Qualifications
- 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.
Leadership Competencies
- Strong problem-solving and analytical skills.
- Ability to translate complex AI concepts into business value.
- Excellent communication and stakeholder management skills.
Interested?
If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.
What's in it for you?
Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.
#Dallas
Location(s): United States : Texas : Dallas
Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.
Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Scotiabank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Scotiabank AI Hiring
Scotiabank has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Dallas, TX, US, Dallas, TX.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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