Required Education/Experience:
Bachelor's degree with 5+ years of experience in this capacity
Required Qualifications:
This is Architecture position for AI related projects which require daily communications with business owners and other members of architecture team located in US
Position's Contributions to Work Group:
As a Principal Digital Architect, you will own the 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.
Typical task breakdown:
Own and define solution and platform architectures for large scale, distributed systems from concept through production.
Create architecture that meets 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 rationale and trade offs.
Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency.
Required Technical Skills:
Architectural Thinking: Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade offs.
Technical Leadership: Influence without authority; guide teams through architectural decisions and implementation challenges.
Communication: Clearly articulate complex technical concepts to both technical and non technical stakeholders.
Requirements Analysis: Translate business and non functional requirements into scalable technical designs.
Platform & Application Architecture: Strong foundation in designing modern application and platform architectures using established patterns and standards.
Consideration for top candidates:
Experience defining AI reference architectures and standards for enterprise adoption.
Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non AI solutions.
Proven experience taking AI systems from proof of concept to scaled production use.
Strong programming background in Python and Java, with the ability to reason at code level.
Proven experience designing and building enterprise scale, distributed systems.
Hands on experience with cloud native architectures, including AWS services, containerization, and orchestration (Docker, Kubernetes).
Deep understanding of data architecture: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding.
Experience with modern DevOps practices: CI/CD, infrastructure as code, observability, and automated testing.
Strong API design experience (REST, GraphQL, gRPC), including versioning and documentation.
Ability to evaluate and introduce emerging technologies aligned to business goals.
AI Related Skills:
Hands on experience designing Retrieval Augmented Generation (RAG) architectures, including:
Data ingestion pipelines
Document preprocessing and chunking strategies
Vectorization and embedding models
Query time retrieval, ranking, and context assembly
Deep understanding of embedding techniques, similarity search, and trade offs across:
Vector dimensions
Chunk size and overlap
Latency vs. recall vs. cost
Experience with vector databases and search layers (e.g., managed or self hosted vector stores) and their integration into application architectures.
Experience with Agentic Frameworks
Ability to architect end to end AI workflows, including:
Prompt design and prompt versioning
Context management and memory patterns
Model routing and fallback strategies
Knowledge of LLM lifecycle considerations, including:
Model selection (hosted vs. self hosted)
Fine tuning vs. RAG vs. hybrid approaches
Evaluation, monitoring, and drift detection
Strong understanding of AI system non functional requirements, including:
Performance and latency optimization
Cost controls and token efficiency
Security, data privacy, and guardrails
Experience integrating AI capabilities into existing enterprise platforms via APIs and event driven architectures.
Ability to assess, prototype, and productionize emerging AI technologies aligned to business use cases.