AWS DevOps & AI Engineer

Essential Skills/Experience

• 4–6 years of experience in Amazon Web Services, continuous integration and delivery, Cloud Engineering, or a similar software engineering role.
• Strong hands-on experience using AWS services such as EC2, ECS, S3, Lambda, IAM, CloudWatch, VPC, Load Balancers, SQS, and SNS.
• Experience with automated build and deployment pipelines and automation tools, including Jenkins, GitHub Actions, GitLab CI, and/or Ansible.
• Practical experience working with Infrastructure as Code, preferably Terraform and/or CloudFormation.
• Good understanding of AWS networking, security, IAM, and least privilege principles.
• Strong proficiency with Git/GitHub/Bitbucket and source-code management practices.
• Good knowledge of Linux and Windows infrastructure, including Linux administration, Bash, and shell scripting, strong understanding of Docker and containerization.
• Experience with observability and monitoring tools such as AWS CloudWatch, Datadog, and Grafana.
• Strong proficiency in Python 3.x for automation, scripting, API development, and cloud-native applications.
• Good understanding of software engineering practices including unit testing, code quality, packaging, logging, exception handling, and version control.
• Experience developing or consuming REST APIs and integrating cloud and enterprise services.
• Working knowledge of Generative AI, LLMs, prompt engineering, embeddings, and RAG concepts.
• Hands-on/project experience working on AWS Bedrock or an alternative managed GenAI platform.
• Experience using Python to integrate with LLM APIs, AI services, or foundation models.
• Basic understanding of RAG architecture and vector search.
• Understanding of deploying AI applications using Docker and AWS ECS/EKS/Lambda.
• Foundational knowledge of MLOps/LLMOps, including deployment automation, versioning, monitoring, evaluation, and observability.
• Strong problem-solving perspective with a Lean and efficiency-focused approach.
• Proactive, diligent, and dedicated to achieving goals.
• Collaboration and communication skills with proficiency in engaging effectively across technical teams.
• Capacity to work independently while contributing effectively within a team environment.
• Familiarity with DevOps and Agile principles.
• Understanding of Scrum and Kanban methodologies.
• Strong interest in emerging AI/Generative AI technologies and continuous learning.
• Bachelor’s or master’s degree or equivalent experience in Computer Science, Information Technology, a technical field, or a related subject area, and demonstrated experience across AWS Cloud, DevOps, Python automation, CI/CD, and exposure to AI/Generative AI technologies.

Desirable Skills/Experience

• Exposure to scalable and High Availability (HA) AWS architectures.
• Exposure to Kubernetes/EKS administration, Helm, and GitOps/Argo CD.
• Experience with AWS EFS, RDS, DynamoDB, Secrets Manager, Event Bridge, Service Catalog, and Cost Management.
• Experience implementing automated monitoring, alerting, remediation, and self-healing.
• Exposure to event-driven architectures using EventBridge, SQS, SNS, or Kafka.
• Exposure to AWS SageMaker, Databricks, Snowflake, MLflow, or Hugging Face.
• Exposure to Strands Agents, LangChain, LlamaIndex, or MCP (Model Context Protocol).
• Experience building basic AI agents or tool-enabled LLM applications.
• Experience with AI/LLM evaluation, model performance monitoring, and LLM observability.
• Understanding of prompt/model caching and GenAI inference-cost optimization.
• Familiarity with AI security concepts such as PII protection, prompt injection, access control, and data governance.
• Exposure to Airflow, dbt, Kafka, or other data orchestration technologies.
• AWS certifications such as Cloud Practitioner, Solutions Architect – Associate, Developer – Associate, Data Engineer – Associate, or AI/ML certifications are a plus.

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