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HomeAt Splunk, that means pairing React product work with Python services, operational data, permissions, evaluation, and release controls. Earlier at Deloitte, I built extraction, storage, API, and visualization systems; two removed more than 36,000 hours of annual manual work and cut investment-processing time by 66%.WritingArticles and notes on software engineering, AI engineering, system design, and the trade-offs behind production systems.ProjectsSelected systems, the constraints that shaped them, and the trade-offs I would revisit.Abdul AhadI’m an AI full-stack engineer with more than five years of experience building data-intensive enterprise software and production AI systems. At Splunk, I drove AI Service and KPI Discovery from an early prototype through technical design, validation, and production delivery. The capability analyzes operational data and recommends service models and health signals within Splunk IT Service Intelligence.ContactIf you’re hiring for a role, comparing architecture options, or building something with a difficult failure mode, send enough context to make the conversation useful.ColophonHow this portfolio handles static publishing, search, contact delivery, analytics, and releases.Capacity Planning Before Architecture DiagramsA practical method for turning workload assumptions into request, storage, bandwidth, concurrency, and failure-capacity budgets before choosing an architecture.Design Modules Around Change, Not LayersA practical guide to information hiding, volatile decisions, coupling, cohesion, dependency direction, and avoiding abstractions that preserve the wrong boundaries.From Model Call to Reliable AI WorkflowHow one model request grows into retrieval, tools, deterministic workflows, agents, evaluation, observability, and human escalation.Queues, Backpressure, and Idempotency: Designing for FailureHow bounded queues, admission control, idempotency keys, retry budgets, and dead-letter handling turn asynchronous delivery into a controlled reliability system.Refactoring a Legacy Backend Without a Big-Bang RewriteA staged method for understanding, testing, isolating, replacing, observing, and finally deleting legacy backend behavior without one irreversible cutover.MCP in Production: Trust Boundaries, Permissions, and Tool DesignA production-focused guide to MCP hosts, clients, servers, capability discovery, authorization, consent, token audiences, and safely designed tools.A portfolio built as a publishing systemA static-first technical publication paired with a rate-limited, AI-assisted contact workflow that is queued, retried, idempotent, and recoverable.ThreadsA discussion application with threaded posts, comments, profiles, and community spaces.Prompt NexusAn open-source application for creating, finding, and sharing prompts for AI tools.SeedA statically generated blogging application backed by a headless content system.CactaA short-video social application with publishing, discovery, profiles, and audience interactions.System DesignCapacity, queues, failure handling, and distributed-system trade-offs.AI EngineeringLLM workflows, tools, evaluation, permissions, and operations.Software EngineeringModules, testing, refactoring, interfaces, and maintainability.System Design from First PrinciplesCapacity planning, queues, backpressure, retries, and failure handling.Production AI EngineeringModel calls, workflows, tools, evaluation, permissions, and operations.

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AI Engineering

LLM workflows, tools, evaluation, permissions, and operations.

article··10 min read

From Model Call to Reliable AI Workflow

How one model request grows into retrieval, tools, deterministic workflows, agents, evaluation, observability, and human escalation.