Web Application Architect

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.

• 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, ship 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 perform 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.

• Bachelor’s degree in a 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 with cloud technologies (AWS, Serverless, Event-Driven Architecture, SOA, Microservices, Microfront ends, CI/CD, Infrastructure as Code, and other modern technologies).
• Demonstrated experience with 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 life cycle, effectively combining human architectural judgment with AI-assisted implementation to accelerate delivery while maintaining quality.

• 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,…