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Gen AI Developer​/Lead

Job in Santa Fe, Santa Fe County, New Mexico, 87503, USA
Listing for: Cognizant
Full Time position
Listed on 2026-09-03
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 156000 USD Yearly USD 100000.00 156000.00 YEAR
Job Description & How to Apply Below

* 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 Lang Chain, Lang Graph, 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 Py Spark 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 Lang Chain, Lang Graph, 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 Dev Ops

  • 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

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