Senior AI/ML Lead/ Developer

Cognizant; Washington (District of Columbia); Presencial; USD 150.000 - 230.000; Tempo integral

Resumo da vaga

Cognizant Technology Solutions Corporation in Washington, DC seeks a Senior AI/ML Engineer to design and deliver analytics and AI-driven solutions for global investment banking operations. You will leverage Python, PySpark, Azure ML, Generative AI, and full stack development to build scalable platforms.

The role emphasizes cloud-native AI workloads, LangChain/LangGraph, RAG, and enterprise-grade governance, collaborating with business stakeholders to turn complex data into actionable insights.

Qualificações

• Experience designing and deploying enterprise ML/AI solutions.
• Strong Python and PySpark development skills.
• Hands-on with cloud platforms and AI services (Azure).
• Familiarity with Generative AI, LangChain, RAG, and agent frameworks.
• Ability to translate business problems into analytics, models, and workflows.

Responsabilidades

• Design, develop, and deploy ML models analyzing large financial datasets.
• Build GenAI solutions using Azure OpenAI Service and RAG apps.
• Develop backend services and APIs using FastAPI for AI workloads.
• Create scalable data pipelines and model deployment workflows in Azure ML.
• Collaborate with stakeholders to govern, validate, and operationalize AI applications.

Conhecimentos

Python

PySpark

Azure Machine Learning

Generative AI

Full Stack Development

LangChain

LangGraph

RAG

FastAPI

Azure OpenAI Service

Ferramentas

Azure Databricks

Azure Data Lake

Azure DevOps

Descrição da vaga

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.