10 days ago
Washington, DC, USAStaff+
Base Salary
$90k - $150k/yr
Responsibilities
- Design, develop, validate, deploy, and optimize machine learning models for large-scale financial datasets and use cases including prediction, classification, clustering, anomaly detection, forecasting, and risk modeling.
- Build enterprise Generative AI, RAG, NLP, LLM, conversational AI, and agent-based solutions using Azure OpenAI Service, LangChain, LangGraph, and agentic AI frameworks.
- Develop scalable backend services, APIs, microservices, integrations, authentication, monitoring, logging, and performance improvements using Python and FastAPI.
- Design data pipelines, feature engineering workflows, reusable data products, model components, monitoring, retraining, experiment tracking, and lifecycle management processes.
- Manage AI/ML deployment, governance, monitoring, and operationalization across Azure Machine Learning and other Azure platforms.
- Collaborate with product owners, business analysts, operations teams, and technology stakeholders to translate financial business challenges into analytical solutions.
- Promote responsible AI through evaluation of fairness, explainability, bias, security, data quality, regulatory compliance, and model governance.
- Mentor junior data scientists, machine learning engineers, and developers while promoting software engineering, MLOps, experimentation, and AI governance best practices.
Requirements
- 8+ years of experience in data science, machine learning, AI engineering, or related fields.
- Expert-level Python and PySpark proficiency for large-scale data processing and model development.
- Strong experience with FastAPI, REST APIs, microservices architecture, object-oriented programming, and software engineering best practices.
- Hands-on experience with LangChain, LangGraph, RAG architectures, agentic AI frameworks, and LLM application development.
- Strong expertise in Azure Machine Learning, Azure OpenAI Service, Azure Databricks, Azure Data Lake, MLOps, and CI/CD practices.
- Deep understanding of supervised and unsupervised learning, deep learning, ensemble methods, NLP, time-series forecasting, anomaly detection, risk modeling, model evaluation, feature engineering, experimentation, validation, and explainability.
- Experience developing and deploying enterprise-grade AI/ML solutions in cloud environments.
- Prior experience with investment banking, capital markets, brokerage operations, trade surveillance, risk management, or front-office and middle-office functions is required or preferred by the posting.
- Understanding of financial products, market data, and regulatory expectations is highly desirable.
- Strong communication, stakeholder management, analytical, problem-solving, and technical explanation skills, including experience with distributed and hybrid teams.
- Preferred experience includes vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB; Docker and Kubernetes; CI/CD and DevOps practices; Responsible AI and model governance; and Azure AI, Data Science, or Machine Learning certifications.
Benefits
- Medical, dental, vision, and life insurance.
- Paid holidays and paid time off.
- 401(k) plan and contributions.
- Long-term and short-term disability coverage.
- Paid parental leave.
- Employee Stock Purchase Plan.
- Applications are accepted until September 7, 2026.
Tech Stack
Categories
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.