10 days ago
Washington, DC, USAStaff+
Base Salary
$100k - $156k/yr
Responsibilities
- Design, develop, validate, optimize, and deploy machine learning models for financial datasets, including predictive, classification, clustering, anomaly detection, forecasting, and risk models.
- Build enterprise generative AI, RAG, LLM, conversational AI, and agent-based solutions using Azure OpenAI Service, LangChain, LangGraph, and agentic AI frameworks.
- Develop scalable backend services, APIs, microservices, AI application frameworks, integrations, authentication, monitoring, logging, and performance optimizations using FastAPI.
- Create scalable data pipelines, feature engineering workflows, reusable data products, experiment tracking, model monitoring, retraining strategies, and lifecycle management processes.
- Operationalize AI and ML workloads on Azure while ensuring security, reliability, scalability, governance, compliance, and production readiness.
- Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define initiatives and present analytical recommendations.
- Promote responsible AI through evaluation of fairness, explainability, bias, security, data quality, risks, assumptions, and regulatory requirements.
- Mentor junior data scientists, machine learning engineers, and developers and promote best practices in software engineering, experimentation, 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, API development, 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 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 deploying enterprise AI/ML solutions in cloud environments and supporting investment banking, capital markets, brokerage operations, trade surveillance, risk management, or front-office and middle-office functions.
- Understanding of financial products, market data, and regulatory expectations is highly desirable.
- 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.
- Strong communication, stakeholder management, analytical, problem-solving, and technical-explanation skills are required.
Benefits
- Annual incentive eligibility through Cognizant’s discretionary performance-based program.
- Medical, dental, vision, and life insurance.
- Paid holidays, paid time off, and paid parental leave.
- 401(k) plan and contributions.
- Long-term and short-term disability coverage.
- Employee Stock Purchase Plan.
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.