Job Role: AI Native Development lead/ Architect
Job Location: Atlanta GA (Hybrid)
Job Type: Contract
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/ 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:
- Python: strong hands-on experience building services and data workloads using Python PySpark AWS Glue and FastAPI.
- C#/.NET: ability to design and services and libraries; familiarity with 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.
Job Role: AI Native Development lead/ Architect Job Location: Atlanta GA (Hybrid) Job Type: Contract 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 enabl...
Job Role: AI Native Development lead/ Architect
Job Location: Atlanta GA (Hybrid)
Job Type: Contract
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/ 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:
- Python: strong hands-on experience building services and data workloads using Python PySpark AWS Glue and FastAPI.
- C#/.NET: ability to design and services and libraries; familiarity with 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.
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