- Design, develop, and deploy advanced machine learning models using Python and PySpark to analyze large-scale financial datasets and generate actionable business insights.
- Build predictive, classification, clustering, anomaly detection, forecasting, and risk models supporting investment banking and brokerage functions.
- Perform rigorous model validation, back-testing, and experimentation using historical and simulated market data.
- Evaluate and implement appropriate statistical, machine learning, deep learning, and AI techniques based on business requirements and regulatory considerations.
- Optimize model performance through feature engineering, hyperparameter tuning, algorithm enhancements, and distributed computing techniques.
- Design and implement enterprise-grade Generative AI solutions using Azure OpenAI Service.
- Build and deploy Retrieval-Augmented Generation (RAG) applications leveraging vector databases and knowledge retrieval systems.
- Develop intelligent agent-based systems using LangChain, LangGraph, and Agentic AI frameworks to automate business workflows and enhance decision support.
- Apply Natural Language Processing (NLP), Large Language Models (LLMs), document intelligence, and conversational AI to streamline surveillance, reporting, compliance, and advisory functions.
- Ensure safe, responsible, and governed adoption of Generative AI capabilities across the organization.
- Design and develop scalable backend services and APIs using FastAPI.
- Build microservices and AI application frameworks that integrate machine learning and GenAI capabilities into enterprise ecosystems.
- Develop reusable and maintainable software components following modern software engineering best practices.
- Implement API integrations, authentication mechanisms, monitoring, logging, and performance optimization strategies.
- Design and implement scalable data pipelines and feature engineering workflows using Azure Machine Learning and cloud-native services.
- Build reusable data products and machine learning components supporting multiple analytics and AI initiatives.
- Partner with Data Engineering teams to operationalize machine learning models and AI applications.
- Establish model monitoring, retraining strategies, experiment tracking, and lifecycle management processes.
- Ensure solutions are secure, reliable, scalable, and production-ready.
- Develop end-to-end ML and AI solutions using:
- Azure Machine Learning
- Azure OpenAI Service
- Azure Data Lake
- Azure Databricks
- Azure Storage Services
- Azure DevOps
- Manage model deployment, monitoring, governance, and operationalization on Azure platforms.
- Support enterprise-scale AI and analytics workloads while maintaining compliance and security standards.
- Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define high-value data science initiatives.
- Translate complex investment banking and brokerage business challenges into measurable analytical solutions.
- Present recommendations and analytical findings to both technical and non-technical audiences.
- Drive adoption of AI and machine learning solutions through effective communication and stakeholder engagement.
- Promote responsible AI practices by evaluating model fairness, explainability, bias, security, and data quality.
- Document assumptions, risks, methodologies, and limitations in a transparent and accessible manner.
- Ensure adherence to regulatory requirements, model governance frameworks, and enterprise AI policies.
- Mentor junior data scientists, machine learning engineers, and developers.
- Promote best practices in software development, experimentation, MLOps, AI engineering, and model governance.
- Contribute to a culture of innovation, continuous learning, and technical excellence.
- 8+ years of experience in Data Science, Machine Learning, AI Engineering, or related fields.
- Expert-level proficiency in Python and PySpark for large-scale data processing and model development.
- Strong experience with:
- FastAPI
- REST APIs
- Microservices Architecture
- Object-Oriented Programming
- Software Engineering Best Practices
- Hands-on experience with:
- LangChain
- LangGraph
- RAG Architectures
- Agentic AI Frameworks
- LLM Application Development
- Strong expertise in:
- Azure Machine Learning
- Azure OpenAI Service
- Azure Databricks
- Azure Data Lake
- MLOps and CI/CD Practices
- Experience developing and deploying enterprise-grade AI/ML solutions in cloud environments.
- Deep understanding of:
- Supervised Learning
- Unsupervised Learning
- Deep Learning
- Ensemble Methods
- NLP
- Time-Series Forecasting
- Anomaly Detection
- Risk Modeling
- Strong understanding of model evaluation, feature engineering, experimentation, validation, and explainability.
- Prior experience supporting:
- Investment Banking
- Capital Markets
- Brokerage Operations
- Trade Surveillance
- Risk Management
- Front Office or Middle Office Functions
- Understanding of financial products, market data, and regulatory expectations is highly desirable.
- Excellent communication and stakeholder management skills.
- Ability to explain complex technical topics to non-technical audiences.
- Strong analytical and problem-solving capabilities.
- Experience working effectively within distributed and hybrid teams.
- Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB.
- Knowledge of containerization technologies including Docker and Kubernetes.
- Experience with CI/CD pipelines and DevOps practices.
- Exposure to Responsible AI, Model Risk Management, and AI governance frameworks.
- Azure certifications in AI, Data Science, or Machine Learning.
- Medical/Dental/Vision/Life Insurance
- Paid holidays plus Paid Time Off
- 401(k) plan and contributions
- Long-term/Short-term Disability
- Paid Parental Leave
- Employee Stock Purchase Plan
À propos de Cognizant
Cognizant (NASDAQ : CTSH) est un AI Builder et une entreprise de services numériques (ESN) élaborant des solutions complètes d’IA maximisant les investissements pour des résultats concrets. Sa profonde expertise des métiers, des processus et des technologies lui permet d’intégrer dans les systèmes technologiques le contexte unique de chaque organisation de l’ingénierie à la production à l’échelle. Son objectif : améliorer l’efficacité des équipes, créer de la valeur et permettre aux grandes entreprises de rester performantes dans un monde qui évolue rapidement. Pour en savoir plus : cognizant.ai ou @cognizant.
Renseignments suppplémentaires sur l'emploi
Les informations sur la rémunération sont exactes à la date de publication. Cognizant se réserve le droit de modifier ces informations à tout moment, conformément aux lois applicables.
Les exigences linguistiques varient selon les postes, mais nous demandons à tous les candidats d’avoir une connaissance de base de l’anglais afin de faciliter les communications internes à l’échelle de l’entreprise. Pour les postes basés au Québec, une maîtrise de l’anglais est requise puisque vous fournirez des services et collaborerez avec des parties prenantes situées hors de la province, qui ne parlent pas nécessairement le français.
Cognizant est un employeur souscrivant au principe de l’égalité d’accès à l’emploi. Votre candidature et votre dossier ne seront pas examinés en fonction de la race, de la couleur, du sexe, de la religion, des croyances, de l'orientation sexuelle, de l'identité de genre, de l'origine nationale, du handicap, de l'information génétique, de la grossesse, du statut d'ancien combattant ou de toute autre caractéristique protégée telle que décrite par les lois fédérales, provinciales ou locales.
Si vous avez un handicap qui nécessite un aménagement raisonnable pour rechercher une offre d'emploi ou poser une candidature, envoyiez un courriel à [email protected] avec votre demande et vos coordonnées.
