{"id":1290975,"url":"https://alion.io/job/citco-team-lead-ai-engineering","title":"Team Lead, AI Engineering","company":{"id":1907202,"name":"Citco","domain":"citco.com","url":"https://alion.io/company/citco","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":76,"open_postings":89,"ghost_share":0,"stale_share":0.944,"repost_share":0.011,"time_to_fill_p50_days":30,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Halifax, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":80000,"max_usd":163000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":19},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-26T03:36:25Z","employer_posted_date":"2026-09-26","last_verified_at":"2026-10-12T09:08:23Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"About Citco\nCitco 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.\nFor more information about Citco, please visit www.citco.com\nAbout the Team & Business Line\nProprietary 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.\nAs 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.\nOur Quality Model & Ownership\nThis role aligns with the Developer profile within CDI Quality Model. Quality is built in, not tested in.\nThe Platform Team defines requirements, acceptance criteria, service expectations and business risk. \nQuality Engineers define the verification strategy, quality gates, test harnesses and observability. \nAI agents accelerate implementation and testing within approved guardrails. \nThe 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.\n Your Role\nYou will lead a cross-functional AI engineering team responsible for designing, building and supporting secure, cloud-native and AI-enabled applications for financial services.\nYou 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.\nTeam Leadership\nBuild and grow a high-performing AI Engineering team through hiring, coaching, mentoring and capability development.\nTechnical Leadership\nYou 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.\nBusiness Leadership\nPartner with business stakeholders and functional leaders to identify automation opportunities, define AI product roadmaps and prioritize initiatives with measurable business value.\nDocument Intelligence\nDefine the technical roadmap for document intelligence solutions supporting document classification, information extraction, workflow automation, retrieval-augmented generation and AI-assisted operational processes.\nAI Governance\nEstablish governance processes for model evaluation, fine-tuning, responsible AI controls, model monitoring and production risk management.\nInnovation\nEvaluate emerging AI technologies and define standards for adoption across foundation models, agents, retrieval systems and intelligent document-processing platforms.\nDelivery and Quality Ownership\nYou 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.\nMentoring and Collaboration\nYou will mentor engineers, strengthen implementation practices and ensure AI-assisted delivery remains secure, observable, testable and aligned with business intent.\nParticipate in and contribute to all agile team activities. \nLead sprint execution, capacity planning, work allocation, risk management and delivery commitments for the AI engineering team. \nSet technical direction with architects and senior engineers across AI services, machine learning, RAG, document processing, agent workflows, data pipelines, application integration and MLOps. \nEnsure appropriate selection of foundation models, machine-learning approaches, AI services, frameworks and data-processing patterns based on business and operational requirements. \nMaintain developer ownership of technical correctness, including human review of AI-generated code, tests, designs and documentation. \nEstablish an engineering culture focused on clear specifications, meaningful tests, data quality, quality gates, observability and continuous improvement. \nCoach engineers, provide regular feedback, support career development and address capability gaps. \nPartner with Platform and Quality Engineering leaders on requirements readiness, risk-based quality investment, acceptance evidence and release planning. \nOversee AI testing and evaluation strategy, including harness architecture, representative datasets, model and prompt evaluation, retrieval validation, regression controls, drift detection and production monitoring. \nEnsure machine-learning and AI solutions have appropriate data validation, fallback behavior, human-review controls and operational support models. \nManage dependencies, escalate blockers early and communicate delivery status and trade-offs to stakeholders. \nLead technical incident coordination, post-mortems, corrective actions and improvements to resilience and support readiness. \nEvaluate emerging AI tools, models, frameworks and practices, introducing them only with appropriate security, governance, cost and quality controls. \nPromote collaboration among AI engineers, data scientists, data engineers, architects, Quality Engineering, security and operations teams.\n About You\nYou must have a Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience. \nTypically 8+ years of software engineering experience, including strong Python, cloud and production-delivery experience. \nDemonstrated leadership of engineering teams delivering AI, machine learning, natural language processing, intelligent document-processing and cloud-native solutions in production environments.\nStrong 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. \nKnowledge of AI and machine-learning frameworks such as LangChain, LlamaIndex, scikit-learn, PyTorch, TensorFlow or comparable technologies. \nUnderstanding of relational, NoSQL and vector-database technologies. \nExperience leading agile delivery, managing priorities and dependencies, mentoring engineers and communicating with senior stakeholders. \nStrong 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. \nExperience with Azure AI, Azure Machine Learning, Azure OpenAI, Google Cloud AI or another cloud provider is beneficial. \nSound judgment in balancing business outcomes, engineering quality, risk, cost and team sustainability. \nDemonstrated experience hiring, coaching, mentoring and managing engineering teams while establishing strong delivery, quality and accountability standards.\nFinancial services or regulated-environment experience is highly desirable. \nExperience defining AI strategies involving large language models, natural language processing, document intelligence, model fine-tuning, retrieval systems and AI governance.\nExperience working directly with business stakeholders to identify opportunities for AI adoption, process automation and operational transformation.\nAI-Native Engineering Expectations\nUse AI coding assistants to accelerate learning, implementation, testing and documentation within team guardrails.\nReview generated code, tests, designs and documentation for correctness, security, performance and intent. \nSet expectations for responsible AI-assisted engineering and ensure human approval remains mandatory for production work. \nEnsure teams have fit-for-purpose harnesses, evaluation criteria, datasets, observability and escalation paths. \nPromote trust-but-verify practices for AI-generated implementation, tests and recommendations. \nUse quality and delivery evidence to improve standards, capability and investment decisions. \nPartner across the Four-Pillar model to preserve clear accountability while increasing delivery velocity.\nOur Benefits\nYour 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.\nWe 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.\nCitco welcomes and encourages applications from people with disabilities. 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