7 days ago
Washington, DC, USAStaff+
Base Salary
$100k - $156k/yr
Responsibilities
- Design, develop, validate, and deploy machine learning models for financial analytics, forecasting, anomaly detection, risk modeling, and operational decision support.
- Build generative AI, RAG, conversational AI, and agent-based solutions using Azure OpenAI Service, LangChain, LangGraph, and related frameworks.
- Develop scalable FastAPI services, APIs, microservices, authentication, monitoring, logging, and integrations for AI applications.
- Create data pipelines, feature engineering workflows, reusable ML components, model monitoring, retraining, experiment tracking, and lifecycle management processes.
- Operationalize AI and machine learning solutions on Azure while ensuring security, reliability, scalability, compliance, and production readiness.
- Collaborate with product owners, analysts, operations teams, and technology stakeholders to translate financial business challenges into analytical solutions.
- Promote responsible AI through fairness, explainability, bias, security, data-quality, governance, and regulatory assessments.
- Mentor junior data scientists, machine learning engineers, and developers and promote technical and engineering best practices.
Requirements
- At least 8 years of experience in data science, machine learning, AI engineering, or related fields.
- Expert proficiency in Python and PySpark for large-scale data processing and model development.
- Strong experience with FastAPI, APIs, microservices, object-oriented programming, and software engineering 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 knowledge 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 deploying enterprise AI/ML solutions in cloud environments.
- Prior experience with investment banking, capital markets, brokerage operations, trade surveillance, risk management, or front- and middle-office functions is required or highly relevant.
- Understanding of financial products, market data, and regulatory expectations is highly desirable.
- Preferred qualifications include experience with Pinecone, Azure AI Search, Weaviate, or ChromaDB; Docker and Kubernetes; DevOps practices; responsible AI and model risk management; and relevant Azure certifications.
- Strong communication, stakeholder management, analytical, problem-solving, and cross-functional collaboration skills are required.
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.
- Eligible for Cognizant’s discretionary annual incentive program based on performance and applicable plan terms.
Tech Stack
Categories
About Cognizant
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value. We build full-stack AI solutions powered by deep industry, process and engineering expertise — embedding an organization's unique context into technology systems that amplify human potential and drive tangible outcomes. From strategy to deployment, we help global enterprises move from AI ambition to AI impact and stay ahead in a fast-changing world. See how at cognizant.ai | Follow us @cognizant