Senior AWS Engineer – AI/ML
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Job Description
Senior AWS Engineer building AI agents, ML pipelines and AWS infrastructure for Cloud Bridge, an AWS Premier Partner. Delivering consultancy projects across GenAI, migrations and modernisation.
Responsibilities:
- Build and deploy AI agents using Amazon Bedrock Agents, Strands framework, Knowledge Bases and Guardrails
- Develop and operate ML training pipelines on Amazon SageMaker
- Implement production infrastructure as Terraform IaC, including Lambda, EventBridge, DynamoDB, S3, SageMaker Pipelines and CloudWatch dashboards
- Build evaluation harnesses and CI-runnable test suites for AI/ML systems
- Implement MLOps pipelines, including model registry, deployment automation, drift monitoring, active learning loops and retraining triggers
- Deliver AWS Landing Zone and multi-account environments using Control Tower, Organizations and Terraform
- Contribute to migration and modernisation engagements, including server migrations, database migrations, networking and application platform builds
- Design and build data engineering pipelines for ML training data
- Implement security hardening for AI and infrastructure workloads
- Produce technical documentation, architecture diagrams, runbooks, operational handover material and findings reports
- Participate in weekly project cadences with Solutions Architects, Project Managers and customer stakeholders
Requirements:
- 3+ years hands-on experience building solutions on AWS, including AI/ML workloads (Amazon Bedrock, SageMaker, or equivalent cloud ML platforms)
- Strong Python engineering skills for production-grade ML pipelines, data processing, API integrations and evaluation frameworks
- Experience with large language models and agentic AI patterns, including prompt engineering, RAG, tool use and agent frameworks
- Solid understanding of EC2, VPC, Lambda, EventBridge, DynamoDB, S3, IAM, CloudWatch and RDS
- Infrastructure as Code using Terraform, CloudFormation or CDK
- Experience building CI/CD pipelines and automated testing
- Ability to work within structured delivery teams and deliver to SOW-defined scope and timelines
- Experience with AWS agent frameworks and tooling, including Strands SDK, Amazon Bedrock AgentCore or Amazon Quick
- Practical experience with Amazon SageMaker training jobs, inference endpoints, Pipelines and model registry
- Experience delivering AWS migration programmes
- Experience with AWS Landing Zones, Control Tower and multi-account governance
- Familiarity with ML evaluation methodology, including confusion matrices, confidence calibration, ECE and F1 disaggregation
- Knowledge of security review and threat modelling for AI systems
- AWS certifications such as ML Specialty or Solutions Architect Associate, or equivalent
- Experience delivering within a consultancy or Professional Services environment
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