Principal Technical Consultant - AI/AWS Cloud Platform Engineering

What you will do

Consulting and delivery leadership 

• Work directly with client stakeholders to interpret business and operational challenges and translate them into the right technical solution, whether that is an AWS landing zone, a migration wave, or an agentic platform: which platform fits, what data and governance boundaries apply, and what the first production candidate should be. 

• Lead junior and mid-level consultants on your engagements: set technical direction, review their work, unblock them daily, and develop them into independent delivery owners. 

• Shape engagements alongside AHEAD account teams: scope workstreams, define deliverables, estimate effort, and keep delivery on plan as client priorities shift. 

• Build and maintain strong client relationships beyond the current statement of work, and identify opportunities to expand AHEAD's footprint through upsell and cross-sell. 

• Serve as the senior technical voice in client steering and working sessions, presenting options with tradeoffs and a clear recommendation rather than a menu. 

AWS platform engineering 

• Lead AWS discovery and assessment engagements: current-state architecture reviews, workload and application inventories with AWS Application Discovery Service, dependency mapping, and cloud readiness assessments that inform landing zone and migration roadmaps. 

• Design and deploy enterprise-scale AWS landing zones using AWS Control Tower and Landing Zone Accelerator: multi-account structures under AWS Organizations, service control policies, centralized logging, and IAM Identity Center foundations. 

• Plan and execute migrations to AWS: workload assessment and wave planning with AWS Migration Hub, application migration with AWS Application Migration Service (MGN) and Database Migration Service (DMS), cutover runbooks, and post-migration validation across compute, storage, databases, and containers. 

• Establish hybrid and cross-premises connectivity: AWS Direct Connect, Site-to-Site VPN, and Route 53 Resolver strategies that connect client on-premises environments to AWS. 

• Own cost governance and FinOps practices: AWS Budgets and Cost Anomaly Detection, tagging standards, Savings Plans and Reserved Instance strategy, and showback or chargeback reporting through Cost Explorer. 

AI agent platform engineering and delivery 

• Design and deploy Amazon Bedrock environments for enterprise clients, including network-isolated configurations behind client firewalls: VPC endpoints, AWS PrivateLink, IAM role and resource policy design, and KMS customer-managed keys. 

• Stand up Bedrock AgentCore and Bedrock Agents governance for client accounts: guardrails, model access policies, knowledge base and connector governance, and licensing and capacity guidance. 

• Build promotion pipelines in GitHub Actions that move agents from sandbox to production through automated tests, evaluation thresholds, and human approval gates implemented as environment protection rules. 

• Implement evaluation and safety frameworks: golden datasets, quality, retrieval, and safety evaluators run in CI, Bedrock Guardrails, and compensating controls for model paths where platform content filtering does not apply. 

• Develop agents in code with Bedrock AgentCore and bring-your-own-framework options such as Strands or LangGraph, and establish ALM so agents move between environments by pipeline rather than by hand. 

• Advise clients on model selection and placement across the Bedrock model catalog, including Anthropic and Amazon Nova models, with data residency, in-region inference, and cost as first-class constraints. 

Documentation and enablement 

• Author the design documents, as-builts, build guides, technical roadmaps, and runbooks the client operates from after the engagement ends. 

• Run workshops, enablement sessions, and office hours for client makers, developers, and administrators. 

• Prepare review packages and evidence for client AI governance boards, and keep decision registers current during delivery. 

 

What you bring

• 8+ years in engineering or technical consulting, including 5+ years delivering AWS infrastructure, landing zones, or migrations, and at least 18 months delivering GenAI or agent systems to production. 

• Delivery leadership. You have led small consulting or engineering teams on client work: assigning and reviewing work, mentoring junior consultants, and staying accountable for the engagement outcome, not just your own tasks. 

• Solution framing. You can take an ambiguous client problem and land it as a concrete architecture, whether that is an AWS landing zone, a migration plan, or an agentic system: pattern, platform choice, data boundaries, and the governance path to production. You know when the right answer is not an agent, and when it is not a rebuild. 

• AWS landing zone and migration depth. Hands-on delivery of Control Tower and Landing Zone Accelerator environments, Migration Hub and MGN-based discovery and migration, multi-account network design with Transit Gateway, and workload right-sizing at enterprise scale. 

• AWS platform core services. Hands-on design and implementation across compute (EC2, ECS, EKS), storage (S3, EBS, EFS), networking (VPC, Transit Gateway, PrivateLink), databases (RDS, DynamoDB), containers, IAM, and security (KMS, Security Hub, GuardDuty). You can explain what must exist before a network-injected AWS service can be created and what cannot be changed afterward. 

• Amazon Bedrock, hands-on. The AgentCore runtime, Knowledge Bases, Guardrails, model catalog management, and OpenSearch Serverless for retrieval in isolated environments. 

• Agent frameworks, hands-on. Building agents with Bedrock Agents or bring-your-own-framework tooling such as Strands or LangGraph, IAM-based agent identity, and CI/CD-driven ALM. 

• IaC and CI/CD ownership. Infrastructure as code with Terraform, CloudFormation, or CDK, and GitHub Actions workflows with environments, protection rules, and OIDC federation to AWS. Pipelines and landing zones you built, not ones you used. 

• Evaluation-driven delivery. You promote agents on evaluation results against a baseline, not on demos, and you can design the dataset and thresholds that make that possible. 

• Consulting-grade communication. Design documents and technical roadmaps other people can execute, and the ability to explain and defend technical decisions with client executives, security teams, and governance boards. 

Nice to have

• AWS credentials: Solutions Architect – Professional (SAP-C02), DevOps Engineer – Professional (DOP-C02), Certified AI Practitioner (AIF-C01), Machine Learning Engineer – Associate (MLA-C01), or Security – Specialty. 

• HashiCorp Terraform Associate certification, and GitHub Actions certification (GH-200). 

• Delivery experience in regulated or confidentiality-driven industries such as legal, financial services, or healthcare. 

• AWS Well-Architected Framework review experience, CloudTrail and CloudWatch observability for agent workloads, and Bedrock evaluation tooling. 

• Amazon Connect or low-code administration background. 

• Experience working alongside client AI governance or review boards.