Job Summary

As a Principal Digital Architect, you will own end-to-end architecture solutions for complex systems, balancing scalability, performance, security, and rapid delivery while influencing enterprise technology strategy. This role requires strong technical depth, architectural judgment, and the ability to translate ambiguous business needs into durable, scalable solutions.

Key Responsibilities
• Own and define solution and platform architectures for large-scale, distributed systems from concept through production.
• Create architectures that meet high standards for scalability, performance, resilience, and security.
• Partner closely with business leaders, product owners, engineering managers, and delivery teams to ensure architectural alignment with business outcomes.
• Assess, select, and introduce new technologies, including proof-of-concept development and architectural spikes.
• Establish and enforce architectural standards, patterns, and best practices across platform teams.
• Provide architectural guidance and mentorship to engineering teams, ensuring high-quality implementation.
• Ensure solutions meet security, compliance, and regulatory requirements.
• Produce and maintain clear architecture documentation, including architectural rationale and trade-offs.
• Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency.
• Decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade-offs.
• Influence technical decisions without direct authority and guide teams through architectural decisions and implementation challenges.
• Translate business and non-functional requirements into scalable technical designs.
• Evaluate and introduce emerging technologies aligned with business goals.
• Design and implement AI reference architectures and standards for enterprise adoption.
• Evaluate architectural trade-offs between classical machine learning, LLM-based approaches, and non-AI solutions.
• Take AI systems from proof of concept through scaled production deployment.
• Architect end-to-end AI workflows, including prompt design, prompt versioning, context management, memory patterns, model routing, and fallback strategies.
• Integrate AI capabilities into existing enterprise platforms through APIs and event-driven architectures.
• Assess, prototype, and productionize emerging AI technologies aligned with business use cases.

Required Qualifications
• Bachelor's degree with 5+ years of experience in this capacity.
• Strong architectural thinking skills with the ability to decompose complex problems and develop pragmatic architecture options.
• Strong technical leadership skills with the ability to influence without authority.
• Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
• Strong requirements analysis skills with the ability to translate business and non-functional requirements into scalable technical designs.
• Strong foundation in modern platform and application architecture using established patterns and standards.
• Strong programming background in Python and Java, with the ability to reason at the code level.
• Proven experience designing and building enterprise-scale distributed systems.
• Hands-on experience with cloud-native architectures, including AWS services, Docker, and Kubernetes.
• Deep understanding of data architecture, including SQL and NoSQL databases, data warehouses such as Snowflake, data modeling, replication, and sharding.
• Experience with modern DevOps practices, including CI/CD, infrastructure as code, observability, and automated testing.
• Strong API design experience with REST, GraphQL, and/or gRPC, including API versioning and documentation.
• Hands-on experience designing Retrieval Augmented Generation (RAG) architectures, including data ingestion pipelines, document preprocessing, chunking strategies, vectorization, embedding models, query-time retrieval, ranking, and context assembly.
• Deep understanding of embedding techniques and similarity search, including trade-offs involving vector dimensions, chunk size, overlap, latency, recall, and cost.
• Experience with vector databases and search layers, including managed or self-hosted vector stores and their integration into application architectures.
• Experience with Agentic AI frameworks and end-to-end AI workflow architecture.
• Strong understanding of LLM lifecycle considerations, including model selection, hosted versus self-hosted models, fine-tuning, RAG, and hybrid approaches.
• Experience with AI evaluation, monitoring, and drift detection.
• Strong understanding of AI system non-functional requirements, including performance and latency optimization, cost controls, token efficiency, security, data privacy, and guardrails.