Apps Dev Tech Sr Lead Analyst at Citigroup in Mississauga Ontario Canada

Job description

• We are looking for an experienced Apps Development Group Manager to lead engineering for the Trade Manager Zone (TMZ) platform within the Cash Securities Settlements group, supporting the Equity Growth Initiative. This is a senior engineering leadership role that combines hands-on technical contribution with organizational accountability — you will be expected to stay close to the code and architecture while also owning the platform strategy, engineering standards, and team development across multiple squads.
• You will be responsible for the reliability, scalability, security, and evolution of a mission-critical equity settlement platform, and will play an active role in embedding AI and ML capabilities into both the platform and the engineering workflow.
• Stays hands-on — actively contributes to design, code reviews, and technical problem-solving alongside the team.
• Operates at two levels — comfortable working at the code and architecture level while owning platform strategy and stakeholder relationships.
• Pragmatic about AI — identifies practical, high-value opportunities to apply AI/ML without losing focus on engineering fundamentals.
• Accountable for outcomes — takes ownership of platform quality, reliability, and delivery across the organization.
• Develops people — invests in engineers and managers, building technical capability and leadership depth across the team.
• Communicates clearly — able to translate technical complexity into clear, concise updates for business and senior leadership audiences.

Responsibilities

• Actively participate in system design, architecture reviews, and code reviews across TMZ platform teams.
• Contribute to the design of distributed, fault-tolerant, real-time systems for high-volume, low-latency equity trade processing.
• Write, review, and refactor production-grade code in Kotlin, Java, and Python — setting the technical bar for the team.
• Lead design of event-driven, microservices-based architectures using Kafka or Solace — including message schema design, topic partitioning, consumer group strategies, and fault-tolerant processing.
• Drive low-latency and high-performance system design — leading performance profiling, bottleneck analysis, and optimization of critical platform components.
• Design and govern data architecture across Oracle (SQL) and MongoDB (NoSQL) — schema design, indexing strategies, query optimization, and data consistency patterns.
• Champion trunk-based development, feature flags, and progressive delivery — contributing to CI/CD pipeline design and optimization.
• Produce and review architecture decision records (ADRs) and technical design documents for key platform components.
• Contribute hands-on to AI/ML integration on the TMZ platform — designing AI pipelines, evaluating ML frameworks, and reviewing AI model integration code.
• Lead the implementation of AI-powered platform capabilities — intelligent exception handling, predictive settlement failure detection, anomaly detection on trade flows, and automated reconciliation.
• Evaluate and adopt GenAI and LLM APIs — prototyping and architecting capabilities such as natural language interfaces for trade operations, AI-driven exception summarization, and GenAI-powered operational tooling.
• Use and promote AI-assisted development tools (e.g., GitHub Copilot or equivalent) — driving team adoption and establishing best practices for AI-augmented engineering.
• Lead the design of AI/ML pipelines on real-time trade event streams — working with data engineers and ML practitioners on feature engineering, model serving, and inference architecture.
• Define and enforce AI governance standards — responsible AI practices, model explainability, auditability, and regulatory compliance.
• Identify and prioritize AI adoption opportunities across the platform — focusing on high-value, engineering-led use cases.
• Set and enforce engineering standards across all TMZ teams — TDD, BDD, trunk-based development, CI/CD, secure coding, and observability.
• Conduct code reviews on critical platform components — providing detailed technical feedback that improves team capability over time.
• Drive adoption of AI-powered quality practices — predictive quality analytics, AI-augmented code review, and automated test generation.
• Lead performance engineering — profiling JVM-based applications (Kotlin/Java), identifying hotspots, and optimizing critical settlement processing paths.
• Own the security and compliance posture of the TMZ platform — involvement in threat modeling, vulnerability assessments, and secure design reviews.
• Define engineering metrics and KPIs — DORA metrics, test coverage, MTTR, and platform SLIs — using data to drive continuous improvement.
• Ensure all engineering delivery is aligned to Citi Engineering Excellence Standards — CI/CD, DevOps, cloud-native practices, and modern ways of working.
• Define and own the engineering roadmap for the Trade Manager Zone platform, aligned to the Equity Growth Initiative and Cash Securities Settlements objectives.
• Develop strong domain knowledge in equity trade lifecycle, settlement mechanics, and cash securities processing to inform sound engineering decisions.
• Own the platform's SLA, SLO, and SLI commitments — driving architectural decisions to meet availability, throughput, and latency targets.
• Lead cross-platform integration — governing API contracts, data flows, and system dependencies across the Cash Securities Settlements ecosystem.
• Drive capacity planning and investment prioritization — decisions on platform scaling, technology refresh, and resource allocation.
• Partner with Operations, Risk, Compliance, and Business teams to ensure the platform meets regulatory requirements and business needs.
• Lead and develop multiple engineering teams — building a culture of technical quality, accountability, and continuous learning.
• Own the talent strategy for TMZ engineering — hiring, retaining, and developing engineers with expertise in financial systems, modern engineering, and AI/ML.
• Develop the next generation of technical leads and engineering managers within the organization.
• Mentor senior engineers and managers — providing technical coaching, architecture guidance, and career development support.
• Drive AI upskilling across the team — building capability in AI tooling, prompt engineering, and responsible AI through direct mentorship and practical workshops.
• Act as the primary engineering point of contact for the TMZ platform with senior business, technology, and operations stakeholders.
• Communicate platform health, delivery progress, engineering strategy, and risk clearly to senior leadership.
• Partner with Product, Operations, Risk, Finance, and Compliance to align engineering priorities with business and regulatory needs.
• Represent TMZ engineering in cross-business technology forums and architecture councils.
• Manage vendor and partner relationships relevant to the platform — cloud providers, tooling vendors, and AI/ML partners.

Requirements

• Must-Have Skills
• Kotlin Primary platform language — strong hands-on proficiency; write, review, and architect production-grade Kotlin services
• Python Hands-on expertise in data pipelines, AI/ML integration, scripting, and automation
• Java Extensive hands-on experience in high-throughput, production-grade Java engineering; JVM performance tuning
• Microservices Architecture Hands-on design of microservices ecosystems — service decomposition, API design, inter-service communication
• Event-Driven & Messaging Systems Deep hands-on expertise in Kafka or Solace — topic design, partitioning, consumer groups, exactly-once semantics, high-throughput stream processing
• Low-Latency & High-Performance Computing Hands-on profiling and optimization — JVM tuning, GC optimization, thread management, memory profiling
• High Availability & Fault Tolerance Hands-on design of resilience patterns — circuit breakers, bulkheads, failover, and disaster recovery
• Databases Hands-on expertise in Oracle (SQL) — schema design, query optimization, indexing — and MongoDB (NoSQL) — document modeling, aggregation pipelines, sharding, replica sets
• AI & ML Integration Hands-on experience designing and integrating AI/ML models — model serving, inference pipelines, real-time scoring, feature engineering
• GenAI & LLM Integration Hands-on experience with GenAI tooling and LLM APIs — prototyping, evaluating, and productionizing GenAI capabilities
• Data Engineering Hands-on expertise in data pipelines, streaming data processing (Kafka Streams, Flink, or equivalent), and data quality patterns
• Intelligent Automation Hands-on application of ML to automate exception handling, anomaly detection, and operational workflows
• AI Governance Establishing AI governance standards — explain ability, auditability, bias mitigation, and regulatory compliance
• Cloud-Native Engineering Hands-on experience with AWS, Kubernetes, and Docker — container orchestration, autoscaling, and cloud-native deployment
• CI/CD & DevOps Hands-on design and ownership of CI/CD pipelines aligned to Citi Engineering Excellence Standards
• Observability & AIOps Hands-on experience with distributed tracing, intelligent alerting, and AI-driven observability
• Secure Engineering Hands-on threat modeling, vulnerability assessments, and secure design reviews
• Agile at Scale Agile/SAFe delivery at program level — PI planning, cross-team dependency management, delivery governance
• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred
• Hands-on experience with real-time ML model serving — feature stores, online inference, and model monitoring.
• Familiarity with RAG patterns, vector databases (e.g., Pinecone, Weaviate), or agentic AI frameworks (e.g., LangChain, AutoGen).
• Knowledge of MLOps practices — model versioning, deployment pipelines, and production model monitoring.
• Understanding of regulatory compliance frameworks relevant to securities settlement (e.g., T+1, CSDR).
• Experience with chaos engineering and resilience testing — fault injection and production readiness reviews.
• Knowledge of reconciliation and exception handling patterns in settlement workflows.
• Familiarity with prompt engineering and LLM fine-tuning in an enterprise, regulated context.
• Experience with technology investment governance and engineering budget management.

Skills

Required

• Kotlin
• Python
• Java
• Microservices Architecture
• Kafka or Solace
• Low-Latency & High-Performance Computing
• High Availability & Fault Tolerance
• Oracle (SQL)
• MongoDB (NoSQL)
• AI & ML Integration
• GenAI & LLM Integration
• Data Engineering

Preferred

• Real-time ML model serving
• RAG patterns
• MLOps practices
• Chaos engineering and resilience testing
• Prompt engineering and LLM fine-tuning

Benefits

• Lead engineering for a platform central to global equity trade settlement operations — while staying technically engaged.
• Drive AI and engineering transformation as part of the Equity Growth Initiative, with senior visibility and business impact.
• Work at the intersection of high-throughput financial systems and modern AI capabilities.
• Build and develop a strong engineering team with access to good tooling, platforms, and talent.
• Be part of an engineering culture that values technical depth at every level of seniority.
• $145,100.00 - $217,700.00
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