{"id":1290974,"url":"https://alion.io/job/citco-senior-ai-engineer-2","title":"Senior AI Engineer","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":75,"open_postings":76,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":81000,"max_usd":179000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":70},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"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":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Keras","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-26T03:18:21Z","employer_posted_date":"2026-09-26","last_verified_at":"2026-10-01T12:31:30Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"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 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.\nWithin 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.\nIn 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.\nQuality Model and 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 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.\nYou will also mentor engineers, establish implementation patterns and ensure AI-assisted delivery remains secure, observable, testable and aligned with business intent.\nParticipate in and contribute to all agile team activities. \nOwn technical specifications, solution design and implementation for complex or high-risk AI and machine-learning capabilities. \nDesign scalable AI architectures, including foundation-model integration, retrieval, document processing, agent workflows, model-serving patterns, evaluation, data controls, fallback strategies and human review. \nKey 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.\nEvaluate candidate algorithms, models, prompts, retrieval approaches and frameworks against business needs, accuracy, operational risk and production constraints. \nDefine and implement model adaptation strategies including prompt optimization, retrieval tuning, supervised fine-tuning and evaluation frameworks to improve solution performance.\nLead the design of data preparation, validation, evaluation and deployment processes supporting AI-enabled solutions.\nIdentify data-distribution changes, model degradation, retrieval failures and other conditions that could negatively affect production performance. \nAnalyze model and application errors, determine underlying causes and design improvement strategies. \nSet coding, testing, review, MLOps and operational patterns across Python, ReactJS, AWS, AI frameworks and CI/CD workflows. \nLead reviews of complex AI-generated code and tests, challenge weak assumptions and ensure independent validation against acceptance criteria. \nDefine evaluation strategies for accuracy, grounding, safety, latency, reliability, security, cost, bias and model or data drift. \nDesign and evolve AI evaluation harnesses, regression datasets, automated scoring, model-monitoring capabilities and production observability. \nResolve difficult production issues, lead technical incident response and drive corrective actions. \nMentor junior and mid-level engineers through design reviews, pairing and actionable feedback. \nCollaborate with data scientists, data engineers, architects, Quality Engineering, security and operations teams.\nPartner directly with business stakeholders to understand operational challenges and translate business needs into scalable AI solutions.\nDrive improvements to architecture, developer experience, reusable libraries, automation and engineering standards.\n About You\nYou must have a Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience. \nTypically, 6-8 years of software engineering experience, including 3+ years designing and deploying AI/ML or Generative AI solutions in production.\nDemonstrated delivery of complex AI, machine-learning, generative AI, natural language processing and document-intelligence systems, including solution architecture, production deployment and operational support. \nExperience designing and implementing model fine-tuning, prompt optimization, retrieval tuning and model evaluation frameworks for production AI systems.\nExperience building intelligent document-processing solutions involving OCR, document classification, entity extraction, semantic retrieval and workflow automation.\nStrong experience with AWS and AI platforms such as Amazon Bedrock, Anthropic Claude, OpenAI, Azure OpenAI or comparable enterprise foundation-model services.\nDeep understanding of distributed systems, APIs, data design, security, performance, testing, observability and CI/CD. \nStrong knowledge of RAG, agents, structured outputs, model and prompt evaluation, guardrails and human-in-the-loop controls. \nExperience with AI orchestration frameworks such as LangChain, LlamaIndex or comparable technologies. \nStrong knowledge of machine-learning and data libraries such as scikit-learn, Pandas, NumPy, PyTorch, TensorFlow or Keras. \nExperience with relational databases, NoSQL technologies, vector databases and large-scale data processing. \nPractical experience with data validation, feature engineering, preprocessing, model training, deployment, monitoring and model-error analysis. \nPractical experience designing AI testing approaches and harnesses for deterministic checks, qualitative evaluation, retrieval quality, hallucination risk, document extraction, agent behavior and regression detection. \nFamiliarity with Azure AI, Azure Machine Learning, Azure OpenAI, Google Cloud AI or another cloud provider is beneficial. \nProven ability to make sound technical trade-offs, influence direction and mentor engineers. \nClear written and verbal communication with technical and non-technical stakeholders. \nFinancial services or regulated-environment experience is desirable. \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. \nDesign repeatable evaluations for prompts, retrieval, document extraction, structured outputs and agent workflows. \nUse test harnesses and observable measures to detect regressions in response quality, grounding, extraction accuracy, latency and reliability. \nMaintain traceability from accepted requirements through implementation, tests and release evidence.\nDefine responsible patterns for AI-assisted design, implementation, testing and documentation. \nRequire independent human validation when the same AI workflow generates implementation and tests. \nEstablish measurable evaluation criteria and representative datasets before accepting AI behavior. \nEnsure harnesses cover adversarial, boundary, security, tenant-isolation and degraded-dependency scenarios where relevant. \nUse production feedback and observability to improve models, prompts, retrieval, controls and engineering standards.\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. Accommodations are available upon request for candidates taking part in all aspects of the selection.","description_format":"text","description_chars":9666,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":["Flexible schedule"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Custody & Fund Administration"],"lifecycle":[{"event":"open","at":"2026-09-26T07:50:36Z"}],"liveness":{"score":62,"band":"ok","label":"Likely open","p_open":1,"p_active":0.621,"p_room":1,"age_days":5,"expected_fill_days":22,"reasons":["conf:3","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/citco-senior-ai-engineer-2","json_url":"https://alion.io/job/citco-senior-ai-engineer-2.json","meta":{"generated_at":"2026-10-01T12:32:20Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3930,"day_limit":5000,"remaining_today":1070,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}