Web Application Architect 4

Bloomberg Industry Group•Arlington, TX

About The Position

You will lead the architectural direction for AI and Machine Learning-enabled systems, ensuring scalable, secure, and cost-effective integration of predictive models, LLMs, and intelligent workflows into customer-facing applications. You are an expert at executing business analysis, application design, development, integration and delivery and application maintenance and support. You will propose, develop, and support customer facing web applications as an AI/ML-enabled DevSecOps team member and provide hands-on solutions to meet or exceed customer expectations. You will provide technical guidance; anticipate technical issues at the product level and make architectural and design decisions to avoid them. This role is expected to operate in an AI-augmented development environment, leveraging coding agents and intelligent development tools to accelerate design, implementation, testing, and system evolution across the SDLC. What you will do: Architect, design and deliver high-quality code by promoting and defining INDG best practices. Serve as a strong influencer on technical trends across multiple areas. Deliver and present solutions for large initiatives across multiple verticals. Design, develop scalable, high availability, high performance products with a deep understanding of front end and back-end architectures. Shape broad architecture; ships multiple large services, complex libraries, or major pieces of infrastructure. Identify technology and AI-driven strategic growth opportunities that enable INDG to expand product capabilities and operational efficiency. Lead cross team efforts and projects that span multiple domains and business units. Participate in providing technology roadmap/vision for the team. Collaborate with cross-functional teams and communicate technical solutions to non-technical people across the organization. Participate in special projects and performs other duties as assigned. Architect and scale AI/ML systems across products, including real-time and batch inference on AWS, while implementing MLOps best practices for model lifecycle management, monitoring, evaluation, drift detection, and reliable data engineering pipelines. Drive cross-team adoption of AI-driven automation within core product workflows. Lead adoption of AI-augmented software engineering by integrating coding assistants into development workflows, establishing safe-use standards, and continuously improving team productivity through AI tooling.

Requirements

Bachelor’s degree in related field or equivalent experience.
7 years of software development experience and/or commensurate skills building commercial applications with modern software engineering principles and practices.
Knowledge across multiple technical domains and an understanding of how and why technologies are deployed and utilized at INDG.
A track record of building stability, performance, and scalability across major business-critical systems.
A clear understanding of the relationship between complexity and cost and a record of successfully devising and implementing long-term strategies to lower it.
Demonstrated experience of cloud technologies i.e. AWS, Serverless, Event Driven Architecture, SOA, MicroServices, Microfrontend, CI/CD, Infrastructure as code, and other modern technologies.
Demonstrated experience of professional software engineering practices and the full software development life cycle, including coding standards, architecture/design patterns, code reviews, source control management, build processes, testing, and operations.
Strong data engineering skills, including building and maintaining scalable data pipelines, designing reliable data models, and optimizing data processing workflows in cloud-based environments
Experience designing, deploying, and integrating ML systems or AI-enabled applications, including LLM and retrieval-based solutions, in production environments, with the ability to design scalable model-serving infrastructure and evaluate tradeoffs across accuracy, latency, cost, and maintainability.
Strong understanding of the end-to-end ML lifecycle, from data preparation and training to deployment and monitoring, with familiarity in MLOps and CI/CD for models
Proficient in using AI-powered coding agents throughout the software development lifecycle, effectively combining human architectural judgment with AI-assisted implementation to accelerate delivery while maintaining quality.

Nice To Haves

Experience building AI-assisted automation and agent-based workflows, including vector databases and retrieval-augmented generation systems.
Experience designing and implementing machine-readable knowledge graphs or ontologies, modeling entities, relationships, temporal rule changes, and cross-jurisdiction dependencies to enable structured reasoning and AI-driven decision support
Experience operating in high-velocity engineering environments that prioritize AI-assisted development and rapid iteration.

Responsibilities

Architect, design and deliver high-quality code by promoting and defining INDG best practices.
Serve as a strong influencer on technical trends across multiple areas.
Deliver and present solutions for large initiatives across multiple verticals.
Design, develop scalable, high availability, high performance products with a deep understanding of front end and back-end architectures.
Shape broad architecture; ships multiple large services, complex libraries, or major pieces of infrastructure.
Identify technology and AI-driven strategic growth opportunities that enable INDG to expand product capabilities and operational efficiency.
Lead cross team efforts and projects that span multiple domains and business units.
Participate in providing technology roadmap/vision for the team.
Collaborate with cross-functional teams and communicate technical solutions to non-technical people across the organization.
Participate in special projects and performs other duties as assigned.
Architect and scale AI/ML systems across products, including real-time and batch inference on AWS, while implementing MLOps best practices for model lifecycle management, monitoring, evaluation, drift detection, and reliable data engineering pipelines.
Drive cross-team adoption of AI-driven automation within core product workflows.
Lead adoption of AI-augmented software engineering by integrating coding assistants into development workflows, establishing safe-use standards, and continuously improving team productivity through AI tooling.