Sr. Gen AI/ML Lead/Developer

• 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.

关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。

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