Architect

Job Summary

Responsibilities

• Design robust context engineering architectures that leverage MCP to orchestrate model inputs and outputs for scalable enterprise generative AI applications on AWS Bedrock with strong focus on reliability and maintainability
• Develop end to end context pipelines that efficiently gather transform and enrich heterogeneous data sources so that large language models receive precise structured and safe context for downstream tasks
• Implement reusable Python components that encapsulate context retrieval ranking and formatting logic enabling consistent behavior across multiple AWS Bedrock models and business domains
• Configure and optimize AWS Bedrock resources including model endpoints logging flows and security integrations to deliver low latency cost efficient and compliant generative AI services for internal and external consumers
• Integrate GitHub Copilot into engineering workflows by defining usage patterns guardrails and code review guidelines that enhance developer productivity while preserving code quality and secure coding practices
• Collaborate with product owners data engineers and application teams to translate abstract business goals into detailed technical designs that align MCP flows context engineering patterns and cloud infrastructure components
• Define standards for prompt design context window management and response validation so that generative AI solutions behave in predictable audit ready and value focused ways across different business scenarios
• Create detailed solution blueprints covering data sourcing transformation context storage model interaction and monitoring so implementation teams can deliver consistent and extensible platforms
• Establish observability practices for MCP and context pipelines by defining metrics logs and alerts that highlight data quality issues prompt drift model behavior changes and performance regressions
• Guide teams in adopting hybrid work practices by documenting reference architectures coding guidelines and knowledge sharing materials that allow distributed team members to align effectively on design decisions
• Conduct design and code reviews with a focus on Python based context services infrastructure as code for AWS resources and integration flows to ensure adherence to security performance and maintainability standards
• Partner with security and compliance stakeholders to embed data privacy policies encryption strategies and access controls within MCP flows and AWS Bedrock integrations reducing risk while enabling innovation
• Drive continuous improvement by analyzing production metrics user feedback and incident reports to refine context features prompts and automation pipelines resulting in more accurate and trustworthy solutions

Qualifications

• Possess extensive hands on experience with MCP and context engineering patterns including design of retrieval and enrichment logic that maximizes relevance accuracy and safety of model inputs
• Demonstrate strong proficiency in Python for building microservices automation utilities and integration components that support data ingestion transformation and model interaction workflows
• Bring practical expertise in AWS cloud services with specific experience in AWS Bedrock including configuration of model endpoints networking identity integration monitoring and cost governance
• Show proven experience using GitHub Copilot or similar coding assistants in professional environments including definition of best practices and safe adoption guidelines for development teams
• Exhibit solid understanding of software engineering fundamentals including version control testing strategies code review practices continuous integration pipelines and documentation discipline
• Apply knowledge of data privacy security controls and compliance requirements when designing and deploying generative AI solutions that process sensitive or regulated information across enterprise environments
• Display strong communication and stakeholder collaboration skills to facilitate alignment between technical specialists and business partners while working in hybrid delivery models that span locations and time zones
• Illustrate familiarity with prompt engineering evaluation techniques and model performance tuning for generative AI use cases so that delivered solutions provide measurable value and consistent outcomes

Certifications Required