Gen AI Developer/Lead
- Washington, United States
نُشرت أول أمس
عن الوظيفة
***No Visa Transfer/c2c/Sponsorship available now or in the future, for this role** Job Summary We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong expertise in Python, PySpark, Azure Machine Learning, Generative AI, and Full Stack Development to design and deliver advanced analytics and AI-driven solutions for global investment banking and brokerage operations. The ideal candidate will combine deep technical expertise in machine learning, distributed computing, cloud-native AI platforms, and modern AI frameworks such as LangChain, LangGraph, RAG, Agentic AI Frameworks, FastAPI, and Azure OpenAI Service . This role requires close collaboration with business stakeholders to transform complex financial data into actionable insights that improve decision-making, reduce operational risk, and enhance operational efficiency. Key Responsibilities Machine Learning & Advanced Analytics
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. Generative AI & Agentic Solutions
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. Python Full Stack Development
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. Data Engineering & MLOps
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. Cloud & Azure AI Platform
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. Business Collaboration
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. Governance & Responsible AI
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. Leadership & Mentoring
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. Required Qualifications Technical Skills
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. Machine Learning & AI
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. Domain Experience
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. Soft Skills
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. Preferred Qualifications
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.
Paid holidays plus Paid Time Off
401(k) plan and contributions
Long-term/Short-term Disability
Paid Parental Leave
Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
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. Generative AI & Agentic Solutions
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. Python Full Stack Development
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. Data Engineering & MLOps
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. Cloud & Azure AI Platform
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. Business Collaboration
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. Governance & Responsible AI
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. Leadership & Mentoring
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. Required Qualifications Technical Skills
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. Machine Learning & AI
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. Domain Experience
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. Soft Skills
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. Preferred Qualifications
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.
- Please note this role is not able to offer visa transfer or sponsorship now or in the future* We're excited to meet people who share our mission and who can make an impact in a variety of ways. Don't hesitate to apply—even if you only meet the minimum requirements. Think about your transferable experiences and unique skills that make you stand out. Salary and Other Compensation: The annual salary for this position is between $100,000 $ 156,000+ depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans. Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Paid holidays plus Paid Time Off
401(k) plan and contributions
Long-term/Short-term Disability
Paid Parental Leave
Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.