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
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 design, build and support secure, cloud-native, AI-enabled applications for the financial services industry. Using modern software engineering practices, AI-assisted development, and cross-functional collaboration, you will lead the design and delivery of complex AI powered solutions that ensure clients maintain seamless access to their critical information assets while keeping Citco ahead of industry innovation.
Within the AI Engineering team, you will lead the design and delivery of document intelligence solutions that classify financial and client documents, extract structured information, and automate complex business processes. You will work directly with business stakeholders, architects and engineering teams to design scalable AI-powered solutions leveraging large language models, natural language processing, machine learning, document understanding and agentic workflows.
In this senior role, you will be expected to influence solution architecture, establish implementation patterns, mentor engineers, and drive the successful delivery of AI initiatives from concept through production.
Quality Model and 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.
Your Role
You will work as part of a cross-functional agile team to design, build and support secure, cloud-native and AI-enabled applications. In this senior role, you will lead the technical design and delivery of complex AI and machine-learning capabilities across services, making architecture-level decisions within your area of responsibility.
You will also mentor engineers, establish implementation patterns and ensure AI-assisted delivery remains secure, observable, testable and aligned with business intent.
- Participate in and contribute to all agile team activities.
- Own technical specifications, solution design and implementation for complex or high-risk AI and machine-learning capabilities.
- Design scalable AI architectures, including foundation-model integration, retrieval, document processing, agent workflows, model-serving patterns, evaluation, data controls, fallback strategies and human review.
- Key team member in the design and implementation of intelligent document processing solutions, including document classification, information extraction, semantic search, business workflow automation and document understanding capabilities.
- Evaluate candidate algorithms, models, prompts, retrieval approaches and frameworks against business needs, accuracy, operational risk and production constraints.
- Define and implement model adaptation strategies including prompt optimization, retrieval tuning, supervised fine-tuning and evaluation frameworks to improve solution performance.
- Lead the design of data preparation, validation, evaluation and deployment processes supporting AI-enabled solutions.
- Identify data-distribution changes, model degradation, retrieval failures and other conditions that could negatively affect production performance.
- Analyze model and application errors, determine underlying causes and design improvement strategies.
- Set coding, testing, review, MLOps and operational patterns across Python, ReactJS, AWS, AI frameworks and CI/CD workflows.
- Lead reviews of complex AI-generated code and tests, challenge weak assumptions and ensure independent validation against acceptance criteria.
- Define evaluation strategies for accuracy, grounding, safety, latency, reliability, security, cost, bias and model or data drift.
- Design and evolve AI evaluation harnesses, regression datasets, automated scoring, model-monitoring capabilities and production observability.
- Resolve difficult production issues, lead technical incident response and drive corrective actions.
- Mentor junior and mid-level engineers through design reviews, pairing and actionable feedback.
- Collaborate with data scientists, data engineers, architects, Quality Engineering, security and operations teams.
- Partner directly with business stakeholders to understand operational challenges and translate business needs into scalable AI solutions.
- Drive improvements to architecture, developer experience, reusable libraries, automation and engineering standards.
About You
- You must have a Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience.
- Typically, 6-8 years of software engineering experience, including 3+ years designing and deploying AI/ML or Generative AI solutions in production.
- Demonstrated delivery of complex AI, machine-learning, generative AI, natural language processing and document-intelligence systems, including solution architecture, production deployment and operational support.
- Experience designing and implementing model fine-tuning, prompt optimization, retrieval tuning and model evaluation frameworks for production AI systems.
- Experience building intelligent document-processing solutions involving OCR, document classification, entity extraction, semantic retrieval and workflow automation.
- Strong experience with AWS and AI platforms such as Amazon Bedrock, Anthropic Claude, OpenAI, Azure OpenAI or comparable enterprise foundation-model services.
- Deep understanding of distributed systems, APIs, data design, security, performance, testing, observability and CI/CD.
- Strong knowledge of RAG, agents, structured outputs, model and prompt evaluation, guardrails and human-in-the-loop controls.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex or comparable technologies.
- Strong knowledge of machine-learning and data libraries such as scikit-learn, Pandas, NumPy, PyTorch, TensorFlow or Keras.
- Experience with relational databases, NoSQL technologies, vector databases and large-scale data processing.
- Practical experience with data validation, feature engineering, preprocessing, model training, deployment, monitoring and model-error analysis.
- Practical experience designing AI testing approaches and harnesses for deterministic checks, qualitative evaluation, retrieval quality, hallucination risk, document extraction, agent behavior and regression detection.
- Familiarity with Azure AI, Azure Machine Learning, Azure OpenAI, Google Cloud AI or another cloud provider is beneficial.
- Proven ability to make sound technical trade-offs, influence direction and mentor engineers.
- Clear written and verbal communication with technical and non-technical stakeholders.
- Financial services or regulated-environment experience is desirable.
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.
- Design repeatable evaluations for prompts, retrieval, document extraction, structured outputs and agent workflows.
- Use test harnesses and observable measures to detect regressions in response quality, grounding, extraction accuracy, latency and reliability.
- Maintain traceability from accepted requirements through implementation, tests and release evidence.
- Define responsible patterns for AI-assisted design, implementation, testing and documentation.
- Require independent human validation when the same AI workflow generates implementation and tests.
- Establish measurable evaluation criteria and representative datasets before accepting AI behavior.
- Ensure harnesses cover adversarial, boundary, security, tenant-isolation and degraded-dependency scenarios where relevant.
- Use production feedback and observability to improve models, prompts, retrieval, controls and engineering standards.
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.