Role Summary
We are looking for an AI Native Development Architect to design and guide the build of cloud-native, data- and AI-driven applications on AWS. You will define target architectures, enable engineering teams with reusable patterns and reference implementations, and accelerate delivery using modern AI-assisted development tools.
Key Responsibilities
- Define end-to-end architecture for AI-native products, including application, data, integration, security, and operations on AWS.
- Lead design reviews and provide technical direction across Python and C#/.NET codebases.
- Architect data pipelines and analytical workloads using PySpark and AWS Glue; establish standards for data quality, lineage, and observability.
- Design and implement scalable APIs and microservices using FastAPI (and/or .NET Web APIs) with clear contracts, versioning, and performance SLAs.
- Establish reference architectures for LLM/RAG-enabled capabilities (e.g., retrieval patterns, prompt management, evaluation, guardrails) aligned with organizational policies.
- Partner with Security, Platform, and DevOps teams to implement secure-by-design practices (IAM, secrets, network controls, encryption, threat modeling).
- Define CI/CD, branching, testing, and release practices; improve developer productivity with automation and paved-road templates.
- Champion AI-assisted engineering workflows using tools such as GitHub Copilot, Cursor, and Claude AI while ensuring code quality and compliance.
- Mentor engineers, create technical documentation, and drive adoption of best practices across teams.
Required Skills
Primary Skills
- Python: strong hands-on experience building services and data workloads using Python, PySpark, AWS Glue, and FastAPI.
- C#/.NET: ability to design and review .NET services and libraries; familiarity with modern .NET runtime and patterns.
- AWS: strong understanding of AWS architecture fundamentals (networking, IAM, compute, storage, managed services) and designing for scale, reliability, and cost.
AI Native Development Tools
- Proficiency using AI coding assistants to accelerate development while maintaining engineering rigor: GitHub Copilot, Cursor, Claude AI.
- Ability to establish team guidelines for AI-assisted coding (review standards, secure prompting, IP/compliance awareness, and validation/testing).
Preferred Qualifications
- Experience designing GenAI solutions (RAG, tool/function calling, agents) and implementing evaluation/monitoring approaches.
- Experience with infrastructure as code (e.g., CloudFormation/CDK/Terraform) and container platforms (Docker/ECS/EKS).
- Knowledge of MLOps patterns (model lifecycle, feature stores, experiment tracking) and data governance concepts.
- Strong understanding of observability practices (logs/metrics/traces) and SRE-oriented reliability design.
Soft Skills & Competencies
- Architecture leadership: can balance short-term delivery with long-term platform thinking.
- Clear communication: can translate complex technical decisions for engineering and business stakeholders.
- Hands-on mindset: comfortable prototyping and jumping into code to unblock teams.
- Quality and security focus: promotes testing discipline, secure coding, and operational readiness.
- Collaboration and mentorship: builds alignment, coaches engineers, and scales best practices across squads.
What Success Looks Like (First 90 Days)
Established reference architectures and coding standards for AI-native services; improved delivery throughput via AI-assisted workflows; delivered at least one production-ready blueprint (API + data pipeline) on AWS with strong security, observability, and cost controls