Job Description
Key Responsibilities
Cloud Architecture Engineering (AWS):
Design, build, and operate scalable, secure, highly available AWS workloads (compute, networking, storage, data, serverless).
Develop reference architectures and IaC modules aligned to best practices and guardrails.
DevOps Platform Automation
Implement CI/CD pipelines, automated testing, and
GitOps workflows. Own Infrastructure as Code (Terraform/CDK/CloudFormation), configuration management, and environment provisioning across dev/test/prod.
Observability Reliability
Set up logging, metrics, tracing, and SLOs using CloudWatch.
Drive incident response, postmortems, capacity planning, and reliability improvements.
Security Compliance
Embed security-by-design (IAM, KMS, Secrets Manager), enforce least privilege, and implement threat detection and vulnerability management.
Support compliance needs (e.g., SOC2, ISO 27001, GxP) via policy-as-code and automated controls.
Cost Management FinOps
Monitor and optimize cloud spend with tagging, budgets, RI/SP management, right sizing, and usage analytics. Advise teams on cost efficient architectures.
Data Integration
Build data pipelines (AWS Glue, Step Functions, Lambda, EventBridge) and API integrations (API Gateway, AppSync, ALB/NLB) to support AI workloads and product features.
AI Platform Enablement (Bedrock, GenAI)
Design and operate Amazon Bedrock integrations, model access patterns, prompt and retrieval pipelines, and RAG architectures using AWS native and open tooling.
Agentic AI Orchestration
Implement agentic workflows (tool use, planning, memory) with frameworks (LangChain, AWS Agents for Bedrock) and secure tool adapters (search, code, data).
Manage observation and safety layers.
MLOps For Foundation Models
Establish versioning, evaluation, governance, and rollout practices for prompts, datasets, embeddings, and model variants.
Automate offline/online evaluation, A/B tests, and canary releases.
Cross Functional Collaboration
Partner with product, data science, security, and compliance to translate requirements into robust cloud and AI solutions.
Provide technical documentation and knowledge sharing.
Required Qualifications
Education/Experience:
Bachelor's degree in Computer Science/Engineering or equivalent experience;
Minimum 6-9 Years Of Experience In The IT Industry.
5+ years in cloud engineering/DevOps with 3+ years hands-on in AWS.
AWS Expertise
Proficiency in IAM, VPC, EC2/EKS, Lambda, API Gateway/AppSync,
S3, RDS/Aurora/DynamoDB, CloudWatch, KMS, Secrets Manager, Step Functions, EventBridge, Glue.
DevOps IaC
Strong skills in Terraform (or AWS CDK/CloudFormation), CI/CD
(GitHub Actions/GitLab CI/AWS CodePipeline), containerization (Docker, Kubernetes/EKS), and artifact management.
Security
Solid understanding of cloud security, networking, encryption, key management, least privilege, and policy-as-code (e.g., OPA/AWS Config).
AI Skills
Hands-on with Amazon Bedrock, LLM integration, prompt engineering, RAG pipelines (vector stores like OpenSearch, Aurora, or DynamoDB + embedding), and
agent frameworks (e.g., LangChain, Agents for Bedrock). Experience with model evaluation, guardrails, and content moderation.
MLOps/Governance
Knowledge of versioning (DVC/Git), experiment tracking
(MLflow/SageMaker), feature/embedding stores, A/B testing, and deployment strategies for AI features.
Soft Skills
Strong communication, documentation, collaboration, and ownership mindset. Comfortable working in regulated environments with risk‑based decision making.
