Senior AWS AI Engineer

Keyrus

. Design and implement serverless and cloud-native architectures on AWS .

Posted 9/15/2026full-timeRemote • PortugalSenior💰 €45,000 - €70,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing serverless and cloud-native architectures on AWS, with a strong focus on Generative AI applications and Retrieval-Augmented Generation solutions. Proficient in Python and Boto3, with a solid understanding of AI governance and operational best practices.

Highest-signal resume keywords

AWS Cloud EngineeringGenerative AI Application DevelopmentPython DevelopmentAmazon Bedrock ExpertiseRAG Architecture Design

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Serverless Architecture DesignCloud-Native Architecture ImplementationGenerative AI DevelopmentRetrieval-Augmented Generation SolutionsSemantic Search ImplementationPrompt EngineeringLLM Evaluation MethodologiesAPI DevelopmentProduction-Grade Software SolutionsVector-Based Retrieval

Soft Skills

CollaborationAgile DeliveryClient-Facing Communication

Tools & Technologies

Amazon OpenSearchBoto3AWS LambdaAWS ECSAWS FargateAWS RDSAWS CognitoAWS IAMAWS Step FunctionsTerraform

Certifications & Qualifications

AWS Certifications

Industry Keywords

AI GovernanceMLOpsInfrastructure as CodeAI-Powered ApplicationsMulti-Step Reasoning Workflows

Tech Stack

Tools & technologies

AWSCloudPythonTerraform

About the role

Key responsibilities & impact

Design and implement serverless and cloud-native architectures on AWS
Build and deploy Generative AI applications using Amazon Bedrock, foundation models, Knowledge Bases, Agent capabilities, guardrails, and related AWS services
Develop AI applications and services using Python and Boto3
Design and implement production-grade Retrieval-Augmented Generation solutions using Amazon OpenSearch, Bedrock Knowledge Bases, vector search technologies, and LLMs
Design and optimise RAG pipelines, including chunking strategies, retrieval optimisation, metadata filtering, and context management
Implement semantic search solutions using Amazon OpenSearch
Design and build agent-based architectures using Bedrock Agents and AgentCore capabilities
Develop prompt engineering frameworks and reusable prompting strategies
Implement LLM evaluation methodologies, including LLM-as-a-Judge and automated evaluation frameworks
Design model routing and inference optimisation strategies
Apply GenAI observability, monitoring, and operational best practices
Contribute to AI governance, evaluation, and responsible AI initiatives
Support the industrialisation of AI solutions into scalable production environments
Collaborate with Cloud Engineers, Data Engineers, Architects, and client stakeholders
Contribute to AI engineering best practices, standards, and reusable frameworks

Requirements

What you’ll need

Relevant academic background in Computer Science, Software Engineering, Information Technology, or equivalent practical experience
5+ years of experience in AWS Cloud Engineering, Software Engineering, or Cloud Solution Development
2+ years of hands-on experience building and deploying Generative AI and LLM-based applications
Proven experience designing and implementing cloud-native architectures on AWS
Demonstrated experience implementing at least one production-grade RAG solution
Experience working in agile delivery teams and multidisciplinary technical environments
Professional proficiency in English
Expertise in Amazon Bedrock, including Foundation Models, Knowledge Bases, Bedrock Agents, and AgentCore capabilities
Experience designing, implementing, and optimising RAG architectures, retrieval pipelines, and AI-powered applications
Strong Python and Boto3 development skills
Experience with semantic search, Amazon OpenSearch, vector-based retrieval, metadata filtering, and search optimisation techniques
Understanding of agentic architectures, multi-step reasoning workflows, and orchestration patterns for GenAI applications
Experience with prompt engineering, prompt evaluation, and techniques for improving LLM response quality and reliability
Knowledge of LLM evaluation frameworks, including LLM-as-a-Judge, observability, monitoring, testing, and responsible AI practices
Understanding of LLM FinOps, model routing strategies, inference optimisation, and cost-performance trade-offs
Experience building APIs, integrating enterprise applications, and developing production-grade software solutions using modern engineering practices
French proficiency, AWS certifications, Terraform and Infrastructure as Code, AWS Lambda/ECS/Fargate/RDS/Cognito/IAM/Step Functions, AWS security, MLOps, CI/CD, architecture governance, consulting/client-facing delivery, and international projects are nice to have

Benefits

Comp & perks

Meal allowance: €10.20/day
Flexible benefits plan
Private medical insurance
22 days of annual leave, increasing every 3 years (up to 25 days)
Continuous learning via KLX – Keyrus Learning Experience
A collaborative, international, and human-centred work environment