Major Skill Sets
01) Architecture & Design Patterns
Ability to design microservices, serverless, event-driven, and LLM-augmented architectures for scalability, resilience, and cost-efficiency, whether running on cloud, edge, or hybrid environments.
02) Networking, Security & AI Governance
Solid grasp of VPC design, load balancing, DNS, encryption, IAM, zero-trust models, and secure AI/ML deployment patterns including model isolation, data governance, and access control for sensitive LLM applications.
03) IaaC & ML/AI Platform Automation
Proficiency with Terraform, CloudFormation, Pulumi, and CI/CD pipelines for automating cloud + AI model deployments, containerized inference workloads, and Kubernetes/EKS/AKS GPU environments.
04) Data, Vector Storage & AI Readiness
Experience selecting and tuning relational, NoSQL, lakehouse, and vector database solutions to support retrieval-augmented generation (RAG), real-time analytics, and high-throughput AI pipelines.
05) Observability, Monitoring & AI
Designing comprehensive logging, metrics, tracing (CloudWatch, Prometheus, OpenTelemetry), and LLM performance monitoring: tracking token throughput, latency, GPU utilization, and cost signals.
06) Cost Optimization, FinOps & AI Efficiency
Leveraging savings plans, rightsizing strategies, usage analytics, and LLM inference cost controls to align cloud + AI spend with business goals, preventing overruns in GPU-heavy environments.
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7+
Years of
experience
Core Competencies
I design scalable cloud and AI architectures that align technology with business goals, accelerating growth, enabling intelligent automation, and supporting the next generation of LLM-powered digital products.
Let’s build a cloud + AI foundation that delivers precision, reliability, and long-term value.
Serverless Event‑Driven
& AI Integrations
Microservices on Containers
& LLM-backed APIs
Lakehouse & Real‑Time Analytics
& Real-Time Analytics
Hybrid / Multi‑Cloud Mesh
for AI Workloads
Process
Strategic, MBA‑Infused Delivery Workflow
1. Discover
Understand business goals, user pain points, and technical constraints through stakeholder interviews, data analysis, and market research.
2. Design & Architect
Translate insights into scalable solutions—whether it’s building AI workflows, cloud architectures, or data strategies—ensuring alignment with business objectives.
3. Develop & Deploy
Implement the solution using modern tools and frameworks. Set up cloud infrastructure, train models, or integrate CRM systems like Salesforce for real-time impact.
4. Optimize & Scale
Continuously monitor, refine, and enhance performance through automation, A/B testing, and feedback loops—ensuring long-term value and adaptability.