GEN AI Solution Architect

Job Title: Solution Architect Location: Remote Reports To: Principal Consultant Practice Area: GenAI Position Overview The Solutions Architect is responsible for designing, communicating, and guiding the delivery of modern AI and ML solutions built on AWS. This role sits at the intersection of architecture, data, and product thinking, helping customers move from exploratory ideas to secure, production-ready AI systems that integrate with real business workflows. You will work closely with customers, New Math Data principal consultants, and data/ML engineers to define target architectures, select AWS services and foundation models, design solution patterns, and guide the technical team. You will act as a client s primary technical advisor in diverse AI projects, with a strong focus on pragmatic value, safety, and operational reliability. Customer discovery and solution shaping

• Lead technical discovery with customers to understand business goals, current systems, data landscape, and constraints.
• Translate ambiguous business problems into well-formed AI use cases and architectural options.
• Develop solution briefs, concept diagrams, and roadmap recommendations that balance innovation with risk and cost.

Architecture and design for AI on AWS

• Design end-to-end architectures for AI applications using AWS services such as Amazon Bedrock, SageMaker, Lambda, Step Functions, DynamoDB, API Gateway, OpenSearch, Kinesis, S3, and others.
• Produce high-quality architectural artifacts, including reference architectures, sequence diagrams, deployment topologies, data models, and integration patterns.
• Collaborate with security, compliance, and data privacy stakeholders to align solutions with organizational policies, including identity and access controls, encryption, logging, and data residency.
• Incorporate responsible AI practices, guardrails, content filters, and human-in-the-loop review patterns into solution designs.
• Advise on strategies for protecting proprietary data and IP.

Technical leadership across delivery

• Support project teams during implementation by clarifying designs, reviewing technical decisions, and removing blockers.
• Review infrastructure as code, API designs, data pipelines, and model integration patterns for alignment with the target architecture and AWS best practices.
• Participate in design reviews, go/no-go decisions for releases, and post-implementation retrospectives.

Presales and stakeholder communication

• Partner with business development, account teams, and product leaders to respond to RFPs, write technical sections, and participate in customer presentations.
• Present complex technical topics in clear, accessible language for both technical and executive audiences.
• Provide thought leadership through demos, workshops, and technical briefings on AI and AWS cloud patterns.

Operational excellence and continuous improvement

• Promote observability practices for GenAI systems: logging, tracing, metrics, evaluation frameworks, and feedback loops.
• Contribute to internal standards, templates, and reference architectures for AI workloads on AWS.
• Stay current with AWS releases, new foundation models, and emerging AI practices, and feed those insights into solution design.

Required Qualifications

• 6+ years of experience in software architecture, cloud architecture, or similar roles, with at least 2 years focused on AI, ML, or GenAI solutions on AWS.
• Bachelor's degree in Computer Science, Software Engineering, MIS, or equivalent combination of education and experience.
• AWS certifications such as AWS Certified Solutions Architect (Professional), Machine Learning Specialty, or Security Specialty.
• Strong hands-on experience with AWS services and AI/ML tools and technologies:
• Core AI and ML: services such as Amazon Bedrock, Bedrock Guardrails, Amazon SageMaker, Amazon Comprehend, and Amazon Kendra.
• Agentic AI: services and tools such as Agentcore, Strands, Langchain.
• Infrastructure as Code: services and tools, such as Terraform, CDK, Cloudformation.
• Data and integration: S3, Glue, Lambda, Step Functions, Kinesis, EventBridge, DynamoDB, RDS or Aurora, OpenSearch.
• Vector databases: OpenSearch, Aurora PostgreSQL with pgvector, or similar.
• MLOps practices: model versioning, CI/CD for ML, feature stores, and model monitoring.
• Testing: unit, functional, and integration testing tools and approaches.
• Proficiency in Python with experience building and integrating APIs or microservices.
• Experience designing secure, production-grade cloud architectures aligned with principles such as least privilege, encryption in transit and at rest, and network segmentation.
• Demonstrated ability to engage with senior stakeholders, gather requirements, and create clear architectural narratives and documents. Strong communication skills, both written and verbal, with the ability to bridge between business and technical audiences.
• Background working in consulting or professional services, driving multiple concurrent customer engagements.
• Experience in one or more of the following sectors: public sector, utilities, healthcare, financial services, or energy, particularly where AI interacts with regulated or safety-critical processes.

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