Gen AI Developer/Lead

Cognizant; Juneau (AK); On-site; USD 100,000 - 156,000; Full time

Benefits offered by this job

Medical/Dental/Vision/Life Insurance

401(k) plan and contributions

Paid holidays and PTO

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant seeks a Senior AI/ML Engineer to design and deliver advanced analytics and AI-driven solutions for global investment banking and brokerage operations. The role combines ML, GenAI, and full stack development using Python, PySpark, and Azure ML.

You will collaborate with stakeholders to transform complex financial data into actionable insights, build scalable AI applications, and ensure governance and responsible AI practices across deployments.

Qualifications

• 8+ years of experience in Data Science, ML, AI Engineering.
• Expert-level proficiency in Python and PySpark.
• Strong experience with FastAPI, REST APIs, and microservices.
• Hands-on with LangChain, LangGraph, RAG, and Agentic AI frameworks.
• Experience deploying enterprise-grade AI/ML solutions in cloud environments.

Responsibilities

• Design, develop, and deploy advanced ML models for financial data.
• Build Generative AI solutions using Azure OpenAI Service.
• Develop enterprise-grade AI/ML pipelines and MLOps workflows.
• Collaborate with product and business stakeholders to translate needs into analytics.

Skills

Python

PySpark

Azure Machine Learning

Generative AI

Full Stack Development

LangChain

LangGraph

RAG

Agentic AI Frameworks

LLMs

Tools

FastAPI

REST APIs

Microservices Architecture

Docker

Kubernetes

Azure OpenAI Service

Azure Databricks

Azure Data Lake

Azure DevOps

Job description

*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.

Please note this role is not able to offer visa transfer or sponsorship now or in the future

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:

• Medical/Dental/Vision/Life Insurance

• 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,