Team Lead – AI Engineering

About Citco

Citco is a global leader in fund services, corporate governance and related asset services with staff across 80 offices worldwide. With more than $1 trillion in assets under administration, we deliver end-to-end solutions and exceptional service to meet our clients’ needs. 

For more information about Citco, please visit www.citco.com

About the Team & Business Line

Proprietary software solutions and innovation are at the core of what differentiates Citco in the alternative investment space. Through our network of global development centres, Citco invests heavily in technology development, security, and infrastructure to ensure our clients continue to receive award-winning products that underpin our commitment to service excellence. 

As a core member of our technology team, you will work with dedicated professionals to build and support secure, cloud-native and AI-enabled applications for the financial services industry. Our AI Engineering teams use modern software engineering practices, AI-assisted development, and cross-functional collaboration to deliver reliable solutions while maintaining clear human ownership of quality and to ensure our clients maintain access to their critical information assets while keeping Citco ahead of industry trends.

Our Quality Model & Ownership

This role aligns with the Developer profile within CDI Quality Model. Quality is built in, not tested in. 

• The Platform Team defines requirements, acceptance criteria, service expectations and business risk. 
• Quality Engineers define the verification strategy, quality gates, test harnesses and observability. 
• AI agents accelerate implementation and testing within approved guardrails. 
• The AI Engineer remains accountable for technical correctness, human review of AI-generated output, meaningful tests, secure implementation and production readiness. AI-generated work never self-approves.

About You

• You must have a Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience. 
• Typically 8+ years of software engineering experience, including strong Python, cloud and production-delivery experience. 
• Demonstrated leadership of engineering teams delivering AI, machine learning, natural language processing, intelligent document-processing and cloud-native solutions in production environments.
• Strong cloud experience, preferably AWS, with practical experience building and deploying AI-enabled applications. Experience with Amazon Bedrock, Anthropic APIs, Azure OpenAI, or comparable foundation-model platforms is relevant.Strong understanding of AI solution architecture, machine-learning lifecycle management, data quality, software quality, security, DevOps, observability and operational risk. 
• Knowledge of AI and machine-learning frameworks such as LangChain, LlamaIndex, scikit-learn, PyTorch, TensorFlow or comparable technologies. 
• Understanding of relational, NoSQL and vector-database technologies. 
• Experience leading agile delivery, managing priorities and dependencies, mentoring engineers and communicating with senior stakeholders. 
• Strong knowledge of AI testing approaches and harnesses, including model and prompt evaluation, RAG validation, document-extraction validation, agent testing, safety controls, regression datasets, drift detection and monitoring. 
• Experience with Azure AI, Azure Machine Learning, Azure OpenAI, Google Cloud AI or another cloud provider is beneficial. 
• Sound judgment in balancing business outcomes, engineering quality, risk, cost and team sustainability. 
• Demonstrated experience hiring, coaching, mentoring and managing engineering teams while establishing strong delivery, quality and accountability standards.
• Financial services or regulated-environment experience is highly desirable. 
• Experience defining AI strategies involving large language models, natural language processing, document intelligence, model fine-tuning, retrieval systems and AI governance.
• Experience working directly with business stakeholders to identify opportunities for AI adoption, process automation and operational transformation.

AI-Native Engineering Expectations

• Use AI coding assistants to accelerate learning, implementation, testing and documentation within team guardrails.
• Review generated code, tests, designs and documentation for correctness, security, performance and intent. 
• Set expectations for responsible AI-assisted engineering and ensure human approval remains mandatory for production work. 
• Ensure teams have fit-for-purpose harnesses, evaluation criteria, datasets, observability and escalation paths. 
• Promote trust-but-verify practices for AI-generated implementation, tests and recommendations. 
• Use quality and delivery evidence to improve standards, capability and investment decisions. 
• Partner across the Four-Pillar model to preserve clear accountability while increasing delivery velocity.

Our Benefits

Your well being is of paramount importance to us, and central to our success. We provide a range of benefits, training and education support, and flexible working arrangements to help you achieve success in your career while balancing personal needs. Ask us about specific benefits in your location. 

We embrace diversity, prioritizing the hiring of people from diverse backgrounds. Our inclusive culture is a source of pride and strength, fostering innovation and mutual respect.

Citco welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection.

Your Role

You will lead a cross-functional AI engineering team responsible for designing, building and supporting secure, cloud-native and AI-enabled applications for financial services.

You will establish the technical direction, engineering standards and delivery practices for the Canadian AI Engineering team while partnering closely with business leaders to expand Citco's Document Intelligence platform and AI-enabled operational capabilities.

Team Leadership

Build and grow a high-performing AI Engineering team through hiring, coaching, mentoring and capability development.

Technical Leadership

You will guide the delivery of cloud-native, machine-learning and generative AI solutions, ensuring architecture decisions, implementation patterns and engineering standards are fit for production.

Business Leadership

Partner with business stakeholders and functional leaders to identify automation opportunities, define AI product roadmaps and prioritize initiatives with measurable business value.

Document Intelligence

Define the technical roadmap for document intelligence solutions supporting document classification, information extraction, workflow automation, retrieval-augmented generation and AI-assisted operational processes.

AI Governance

Establish governance processes for model evaluation, fine-tuning, responsible AI controls, model monitoring and production risk management.

Innovation

Evaluate emerging AI technologies and define standards for adoption across foundation models, agents, retrieval systems and intelligent document-processing platforms.

Delivery and Quality Ownership

You will be accountable for team execution and technical quality, balancing delivery speed with sustainable engineering practices, clear human review and responsible use of AI agents.

Mentoring and Collaboration

You will mentor engineers, strengthen implementation practices and ensure AI-assisted delivery remains secure, observable, testable and aligned with business intent.

• Participate in and contribute to all agile team activities. 
• Lead sprint execution, capacity planning, work allocation, risk management and delivery commitments for the AI engineering team. 
• Set technical direction with architects and senior engineers across AI services, machine learning, RAG, document processing, agent workflows, data pipelines, application integration and MLOps. 
• Ensure appropriate selection of foundation models, machine-learning approaches, AI services, frameworks and data-processing patterns based on business and operational requirements. 
• Maintain developer ownership of technical correctness, including human review of AI-generated code, tests, designs and documentation. 
• Establish an engineering culture focused on clear specifications, meaningful tests, data quality, quality gates, observability and continuous improvement. 
• Coach engineers, provide regular feedback, support career development and address capability gaps. 
• Partner with Platform and Quality Engineering leaders on requirements readiness, risk-based quality investment, acceptance evidence and release planning. 
• Oversee AI testing and evaluation strategy, including harness architecture, representative datasets, model and prompt evaluation, retrieval validation, regression controls, drift detection and production monitoring. 
• Ensure machine-learning and AI solutions have appropriate data validation, fallback behavior, human-review controls and operational support models. 
• Manage dependencies, escalate blockers early and communicate delivery status and trade-offs to stakeholders. 
• Lead technical incident coordination, post-mortems, corrective actions and improvements to resilience and support readiness. 
• Evaluate emerging AI tools, models, frameworks and practices, introducing them only with appropriate security, governance, cost and quality controls. 
• Promote collaboration among AI engineers, data scientists, data engineers, architects, Quality Engineering, security and operations teams.