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Hays

Artificial Intelligence Engineer

Halifax, NS, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Experience: 8+ years of software engineering experience.
Posting language: English
Posted: Sep 28, 2026

Apply About employer

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Original job description

Team Lead, AI Engineering / Senior AI Engineer

Job Type: Permanent

Location: Halifax, NS

Work Model: Hybrid (3 Days Onsite per Week)

Compensation: CAD $120,000 - $175,000 + Benefits

About the Opportunity:

Our client is seeking experienced AI Engineering professionals to join a growing technology organization focused on delivering secure, cloud-native, and AI-enabled solutions within a highly regulated environment.

Opportunities are available at both the Senior AI Engineer and Team Lead, AI Engineering levels. Successful candidates will play a key role in designing, building, scaling, and supporting enterprise AI solutions while working closely with business stakeholders, engineering teams, quality specialists, and platform teams.

The organization is investing heavily in AI, automation, intelligent document processing, machine learning, and cloud-native engineering, providing an opportunity to work on cutting-edge technologies in a production environment.

Team Lead, AI Engineering

What You'll Do

Team Leadership

Build, mentor, and develop a high-performing AI Engineering team.
Lead hiring, coaching, performance management, and capability development activities.
Promote engineering excellence, accountability, collaboration, and continuous improvement.

Technical Leadership

Define technical direction and implementation standards for AI-enabled applications.
Lead the delivery of cloud-native, machine learning, generative AI, and intelligent automation solutions.
Collaborate with architects and senior engineers to establish scalable and production-ready architectures.

Business Partnership

Partner with business stakeholders to identify automation opportunities and define AI roadmaps.
Prioritize initiatives that deliver measurable business outcomes and operational improvements.
Communicate technical concepts and implementation trade-offs to both technical and non-technical audiences.

Document Intelligence & Generative AI

Define and guide document intelligence solutions involving:
Document Classification
Intelligent Data Extraction
Workflow Automation
Retrieval-Augmented Generation (RAG)
AI-Assisted Operational Processes

AI Governance

Establish governance and controls surrounding:
Model Evaluation
Prompt Evaluation
Fine-Tuning
Responsible AI
Monitoring & Drift Detection
Production Risk Management

Delivery & Engineering Excellence

Lead sprint planning, capacity planning, work allocation, and release activities.
Ensure AI engineering solutions are secure, observable, testable, maintainable, and production-ready.
Drive engineering quality through meaningful testing, monitoring, validation, and operational readiness.
Lead production incident management, post-mortems, and remediation activities.

What You'll Need

Required

8+ years of software engineering experience.
Strong hands-on Python development experience.
Demonstrated leadership experience managing engineering teams.
Experience delivering AI, Machine Learning, NLP, Document Intelligence, or Intelligent Automation solutions in production environments.
Strong cloud experience, preferably AWS.
Experience building and deploying AI-enabled applications.
Experience with:
Amazon Bedrock
Anthropic APIs
Azure OpenAI
Google Cloud AI
Comparable foundation model platforms
Deep understanding of:
AI Architecture
Machine Learning Lifecycle Management
DevOps
Data Quality
Security
Observability
Operational Risk
Experience with:
LangChain
LlamaIndex
PyTorch
TensorFlow
scikit-learn
Experience working with relational, NoSQL, and Vector Databases.
Strong stakeholder management and communication skills.
Experience mentoring, coaching, and managing engineering teams.
Experience with AI governance, model evaluation, prompt testing, RAG validation, and monitoring frameworks.

Preferred

Financial services or regulated-industry experience.
Experience developing AI strategy and enterprise AI roadmaps.
Exposure to intelligent document processing platforms.
Experience with Azure AI, Azure Machine Learning, Azure OpenAI, or Google Cloud AI services.
Experience supporting operational transformation through AI adoption.

Senior AI Engineer

What You'll Do

Design, develop, and support cloud-native AI applications and services.
Build and maintain AI-powered solutions involving generative AI, machine learning, NLP, and document intelligence.
Develop Retrieval-Augmented Generation (RAG) solutions and intelligent automation workflows.
Integrate foundation models and AI services into enterprise applications.
Build evaluation frameworks and testing harnesses for AI solutions.
Develop data pipelines, application integrations, and operational AI services.
Collaborate with Data Scientists, Software Engineers, Quality Engineers, Architects, and Product teams.
Participate in Agile delivery and support production-ready implementations.
Contribute to observability, governance, monitoring, and operational excellence initiatives.
Review AI-generated code and ensure correctness, security, scalability, and maintainability.

What You'll Need

Strong software engineering background with Python.
Experience delivering cloud-native applications.
Hands-on experience with AI/ML solutions in production.
Experience with:
LangChain
LlamaIndex
Foundation Models
RAG Architectures
NLP Solutions
Knowledge of AI testing approaches, prompt evaluation, and model validation.
Understanding of cloud platforms, preferably AWS.
Experience with modern DevOps practices and CI/CD pipelines.
Strong understanding of software quality, testing, security, and observability.
Ability to collaborate effectively with technical and business stakeholders.

What You'll Get in Return:

Permanent full-time opportunity.
Competitive compensation package.
Salary range of CAD $120,000 - $175,000, depending on experience and level.
Opportunity to work on cutting-edge AI, Machine Learning, RAG, and Intelligent Automation solutions.
Exposure to enterprise-scale cloud-native and AI initiatives.
Collaborative, innovative, and technology-driven work environment.
Hybrid work arrangement (3 days onsite in Halifax).

What You Need to Do Now

If you're interested in this opportunity, please submit your updated resume for consideration.

If this role isn't quite the right fit, but you're actively exploring AI Engineering opportunities, we'd be happy to have a confidential discussion regarding your career goals and future opportunities.

Apply About employer

More about this job

Responsibilities

Lead or contribute to the design, development, deployment, and support of secure, production-ready cloud-native AI solutions, including generative AI, machine learning, document intelligence, and intelligent automation. Depending on level, lead an AI engineering team and set technical direction, or build AI applications and evaluation frameworks while collaborating with business and technical teams.

Requirements

The Team Lead role requires 8+ years of software engineering experience, strong hands-on Python skills, engineering team leadership, and production experience delivering AI solutions; the Senior AI Engineer role requires a strong software engineering background and hands-on production AI/ML experience. Candidates should have cloud and DevOps experience, familiarity with foundation models and RAG tools, and knowledge of AI testing, governance, security, and observability.

Skills

Python
Artificial Intelligence
Machine Learning
Generative AI
Natural Language Processing
Document Intelligence
Retrieval-Augmented Generation
Cloud-Native Engineering
AWS
AI Governance
Model Evaluation
Prompt Evaluation
LangChain
LlamaIndex
DevOps
Engineering Team Leadership

Visa sponsorship

Not detected in the job text

Keywords

Python
AWS
Amazon Bedrock
Anthropic APIs
Azure OpenAI
Google Cloud AI
LangChain
LlamaIndex
PyTorch
TensorFlow
Scikit-Learn
Foundation Models
Retrieval-Augmented Generation
Natural Language Processing
Document Classification
Intelligent Data Extraction
Workflow Automation
AI Governance
Model Evaluation
Prompt Evaluation
Fine-Tuning
Monitoring and Drift Detection
Vector Databases
Relational Databases
NoSQL Databases
DevOps
CI/CD
Observability
Financial Services

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