AI Engineer

Position Summary
We are seeking an AI Engineer to support model transformation initiatives focused on modernizing how models are monitored, governed, reported, and improved. This role will contribute to Model Performance Monitoring, MLOps enablement, reporting automation, dashboard development, backend services, and GenAI-enabled model insights.
The ideal candidate is a hands-on engineer with strong Python backend development skills, practical experience with data and cloud technologies, and an interest in building scalable, secure, and user-friendly platforms for model monitoring and governance. The candidate does not need to have every skill listed below, but should bring experience in several core areas and the ability to learn quickly in an enterprise environment.
Key Responsibilities
Design and develop backend services and APIs to support model performance monitoring, data ingestion, reporting, dashboarding, and workflow integration.
Build RESTful services using modern Python frameworks such as FastAPI, SQLAlchemy, and Pydantic.
Develop data processing capabilities for model monitoring inputs, including JSON, Excel, and relational data sources.
Design and maintain database schemas, data models, and optimized SQL queries using PostgreSQL or similar databases.
Support automated model monitoring reports, executive dashboards, and PDF report generation.
Contribute to interactive dashboards and analytics views that help users evaluate model health, performance trends, drift indicators, and monitoring status.
Support frontend development where needed using Angular, TypeScript, RxJS, and component-based UI patterns.
Integrate visualization tools such as Plotly.js, Chart.js, or Tableau dashboards.
Build cloud-native components using AWS services such as S3, Lambda, and boto3.
Support secure file storage, retrieval, report generation, and data exchange across enterprise systems.
Contribute to DevOps practices including Docker, CI/CD pipelines, Git-based workflows, SonarQube, environment configuration, and deployment automation.
Help develop GenAI-enabled features such as AI-assisted model insights, report summarization, chatbot integration, and prompt-based analytics.
Collaborate with model developers, technology teams, architecture, governance, risk, and business stakeholders to deliver secure, scalable, and audit-ready solutions.
Required Qualifications
Bachelor s degree in Computer Science, Engineering, Data Science, Information Systems, Mathematics, or a related technical field.
Strong hands-on experience with Python and backend application development.
Experience building REST APIs and working with modern API design patterns.
Practical knowledge of relational databases, SQL, schema design, and data modeling.
Experience processing structured and semi-structured data, including JSON and Excel files.
Familiarity with model lifecycle concepts, model monitoring, MLOps, data validation, or AI/ML application development.
Experience with cloud-native development, preferably AWS.
Working knowledge of Git, CI/CD practices, containerization, and deployment workflows.
Strong problem-solving skills, attention to detail, and ability to work across technical and business teams.
Good documentation and communication skills.
Preferred Skills
Candidates should have experience with some of the following:
Backend Development
Python 3.11+
FastAPI
SQLAlchemy 2.0
Pydantic v2
asyncio
REST services and API standards
JWT/OAuth2, PyJWT, and enterprise SSO integration
Database & Data Management
PostgreSQL
SQL query optimization
Database schema design
psycopg2 or asyncpg
Data modeling
JSON and Excel data processing
Reporting & Dashboards
PDF report generation
fpdf2 or WeasyPrint
matplotlib or seaborn
Executive reporting dashboards
Frontend & Visualization
Angular 19
TypeScript
RxJS
SCSS
Component-based UI development
Reactive forms
Responsive UI design
Plotly.js, Chart.js, or Tableau Embedding API
Interactive dashboards and dynamic chart rendering
Cloud, DevOps & Infrastructure
AWS S3
AWS Lambda
boto3 SDK
Docker
NGINX
CI/CD pipelines
SonarQube
Git, GitLab, or GitHub
Environment configuration management
Deployment automation
GenAI & Intelligent Features
GenAI/LLM integration
AI-assisted model insight generation
Chatbot integration
Prompt engineering
Success in This Role
Success in this role means helping deliver practical, reliable, and scalable capabilities that improve how models are monitored and governed. The AI Engineer will help reduce manual reporting effort, improve model performance transparency, strengthen auditability, and enable faster adoption of standardized model transformation capabilities.
The role requires a balance of backend engineering, data management, reporting, cloud development, and AI/ML awareness. Strong candidates will be comfortable building production-quality solutions while collaborating closely with model teams, risk partners, business users, and enterprise technology stakeholders.
At least 5 years experience; prefernce to 7+ years of experience