Job Description

AWS Engineer (AI/ML) - Up to £75k

UK based - Fully Remote

Cloud Bridge is one of the fastest-growing AWS Premier Partners in the UK & EMEA, named AWS Rising Star Partner of the Year (EMEA 2023, UK&I 2022). We specialise in cloud consultancy, migration, managed services, cloud governance, FinOps and AI/ML, helping organisations unlock the full value of AWS. Cloud Bridge Inc (Philippines) is our delivery centre supporting UK, APAC and global engagements.

Role Overview

We are seeking a hands-on AWS Engineer with strong AI/ML capability to join our Professional Services delivery team in the Philippines. You will deliver customer projects across GenAI, machine learning and broader AWS infrastructure – including Landing Zone deployments, migrations and modernisation work alongside AI/ML engagements.

Your primary specialism is AI and ML delivery on AWS – building agents, training pipelines, production infrastructure and evaluation frameworks using Amazon Bedrock, Amazon SageMaker and Terraform. However, you will also contribute to wider AWS engagements as the pipeline requires, applying your infrastructure and IaC skills across the full range of Cloud Bridge delivery.

You will operate within structured SOW-driven delivery teams, taking architectural direction from Solutions Architects while owning the hands-on implementation, testing and documentation of technical deliverables.

Key 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 – data preparation, model fine-tuning, hyperparameter tuning, evaluation and deployment.
• Implement production infrastructure as Terraform IaC – Lambda, EventBridge, DynamoDB, S3, SageMaker Pipelines, CloudWatch dashboards and observability.
• Build evaluation harnesses and CI-runnable test suites for AI/ML systems (precision, recall, calibration, regression detection).
• Implement MLOps pipelines – 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 – server migrations, database migrations, networking and application platform builds as required.
• Design and build data engineering pipelines for ML training data (labelling infrastructure, data curation, train/validation/test splits).
• Implement security hardening for AI and infrastructure workloads – IAM least-privilege, KMS encryption, Bedrock Guardrails, audit logging.
• Produce clear technical documentation – architecture diagrams, runbooks, operational handover material and findings reports.
• Participate in weekly project cadences with Solutions Architects, Project Managers and (where required) customer stakeholders.

Essential Experience & Skills

• 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 – comfortable building production-grade ML pipelines, data processing, API integrations and evaluation frameworks.
• Experience with large language models and agentic AI patterns – prompt engineering, RAG, tool use and agent frameworks.
• Solid understanding of core AWS services: EC2, VPC, Lambda, EventBridge, DynamoDB, S3, IAM, CloudWatch, RDS.
• Infrastructure as Code using Terraform (preferred) or CloudFormation/CDK – able to define and deploy complete AWS environments.
• Experience building CI/CD pipelines and automated testing.
• Comfortable working within structured delivery teams, taking direction from a Solutions Architect and delivering to SOW-defined scope and timelines.

Desirable Experience

• Experience with AWS agent frameworks and tooling – Strands SDK, Amazon Bedrock AgentCore, Amazon Quick.
• Practical experience with Amazon SageMaker – training jobs, inference endpoints, Pipelines, model registry.
• Experience delivering AWS migration programmes (MGN, wave-based migrations, database migrations).
• Experience with AWS Landing Zones, Control Tower, multi-account governance.
• Familiarity with ML evaluation methodology – confusion matrices, confidence calibration, ECE, F1 disaggregation.
• Knowledge of security review and threat modelling for AI systems (prompt injection, data exfiltration, privilege escalation).
• AWS certifications – ML Specialty, Solutions Architect Associate, or equivalent.
• Experience delivering within a consultancy or Professional Services environment.