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AWS DevOps Engineer – SC eligible
Location: London
Working Model: Hybrid (2 or 3 days from client office)
Short Description: AWS Cloud Engineer
We are seeking a highly skilled AWS Cloud Engineer with deep hands-on experience across AWS cloud services and BAU Ops role. This role requires hands-on Cloud Engineering expertise, Networking, AWS cloud migration experience, good communication skills, and product maintenance experience for post go-live operational support.
Your core responsibilities include:
- Implement end-to-end AWS solutions across compute, networking, storage, and analytics, leveraging services such as Fargate, Bedrock, Redshift, ALBs, VPC networking, Neptune DB, and regional AWS deployments.
- Migration of existing AWS infra from Cloud Formation templates to Terraform.
- Migration of CHEF cookbooks to Ansible.
- Migration of Jenkins jobs to GitHub actions.
- Post go-live production BAU Ops role.
- Ensure robust operational standards across logging, security, monitoring, IAM governance, and distributed cloud reliability.
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What skills are required?
Minimum skills:
- Degree in Computer Science, Information Systems, Engineering, or equivalent experience.
- Good architectural understanding of core AWS services, cloud networking, and distributed system design.
- Proven hands-on experience in AWS, CHEF, API development, Ansible, GitHub actions and runners, and Terraform.
Essential skills:
- Strong experience with AWS data and AI ecosystem: Bedrock, Fargate, EFS, Redshift, Neptune, ALBs, VPC networking, CloudWatch, IAM, and regional deployment strategies including software’s such as CHEF, Ansible, Terraform, GitHub.
- Good understanding of cloud security, scalability patterns, and cost-aware solution design.
- Confident communicator with excellent command of English, capable of influencing and leading client conversations.
- Working experience of ServiceNow, Jira, DataDog (or any other platform monitoring tool).
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Good to have:
- Experience with vector databases, graph databases (Neptune), or graph exploration tools.
- Proven hands-on experience in Python, API development, and automation.
- Hands-on background with LLM integrations, GenAI solution architecture, and AI pipeline orchestration.
- Familiarity with orchestration tools, serverless design patterns, or container-native workflows.
- Exposure to modern AI frameworks, LangGraph/LangChain concepts, and prompt engineering.
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