1 month ago
Washington, DC, USAStaff+
Base Salary
$90k - $150k/yr
Responsibilities
- Design, develop, validate, back-test, and deploy predictive, classification, clustering, anomaly detection, forecasting, and risk models using Python and PySpark.
- 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, authentication integrations, monitoring, logging, and AI application components using FastAPI.
- Design data pipelines and feature engineering workflows and operationalize models through monitoring, retraining, experiment tracking, governance, and lifecycle management.
- Deploy and manage production AI and analytics workloads across Azure Machine Learning, Azure Data Lake, Azure Databricks, Azure Storage Services, and Azure DevOps.
- Collaborate with product owners, business analysts, operations teams, and technology stakeholders to translate investment banking and brokerage challenges into analytical solutions.
- Promote responsible AI through evaluation of fairness, explainability, bias, security, data quality, regulatory compliance, and model risk.
- Mentor junior data scientists, machine learning engineers, and developers and promote best practices in software engineering, MLOps, AI engineering, and model governance.
Requirements
- At least 8 years of experience in data science, machine learning, AI engineering, or related fields.
- Expert-level Python and PySpark experience for large-scale data processing and model development.
- Strong experience with FastAPI, REST APIs, microservices architecture, 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 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.
- Experience supporting investment banking, capital markets, brokerage operations, trade surveillance, risk management, or front-office and middle-office functions.
- Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB is preferred.
- Knowledge of Docker, Kubernetes, CI/CD pipelines, DevOps practices, responsible AI, model risk management, and AI governance frameworks is preferred.
- Azure certifications in AI, data science, or machine learning are preferred.
- Excellent communication, stakeholder management, analytical, problem-solving, and technical explanation skills, including within distributed and hybrid teams.
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.
- Remote position; applications accepted until Sept 07, 2026.
- Eligible for Cognizant’s discretionary annual incentive program based on performance and applicable plan terms.
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.