Senior AI Engineer | Agentic AI | RAG | LangGraph | Production AI Systems

Senior AI engineer for production GenAI, RAG, agent workflows, and large-scale software systems.

I build AI platforms that know when to stay simple, when to retrieve, and when to route into bounded agents for safe tool actions, evaluations, observability, and rollback paths. Experience across Meta, Loudegg, Caesars Digital, Wells Fargo, and Tata Consultancy Services.

Production AI architecture RAG + agents when justified Frontend to distributed backend

For recruiters: start here Review AI Work Request Protected Access

5 enterprise environments

Millions of users in large-scale systems

5M+ events/day in real-time systems

40% cost reduction through AI execution strategy

RAG MCP LangGraph Kafka AI Agents

Best fit Senior AI engineer who can own AI systems from architecture to production.

Designs routing, RAG, agents, tools, evals, and guardrails
Ships frontend, backend, queues, caches, and APIs
Explains tradeoffs clearly to engineers and leaders

employer-review.run

> scan public proof: work, systems, impact

> unlock deep dives: architecture on request

> inspect dry runs: data flow + failure paths

My AI engineering philosophy Production AI is 10% model calls and 90% routing, tools, data, evals, observability, and rollout discipline.

Agent leverage Use agents where they save real time and money.

I look for workflows where agents can remove repeated manual effort, coordinate steps, call tools safely, or speed up decisions. If retrieval or normal software solves it cheaper, I do not add agent complexity.

Safety backbone Control comes before autonomy.

Useful agents need permissions, private-data boundaries, typed tool schemas, approval gates, idempotency, audit logs, evals, and observability before they touch real work.

RAG judgment Ground answers before escalating to action.

Many requests should stay RAG-only: retrieve, cite, answer, or refuse. I escalate to agent workflows only when the task needs state, tools, decisions, or long-running work.

Software systems The model is only valuable when the system holds.

APIs, queues, caches, databases, auth, cost budgets, rollout plans, and debugging paths are what turn AI ideas into reliable automation that helps a company move faster.

Meta Wells Fargo Caesars Digital Tata Consultancy Services Loudegg

Agentic AI Judgment Use agents when the workflow needs them

Intent routing, planner-executor flows, MCP tool contracts, human review, idempotency, and audit logs.

RAG Systems From raw docs to grounded answers

Ingestion, chunking, embeddings, hybrid search, reranking, citations, evals, and rollbackable indexes.

Software Systems Frontend, backend, data, and APIs

Typed services, auth, queues, caches, databases, observability, deployment, and user-facing product flows.

Scale Designed for real traffic and failure

Kubernetes pods, Kafka backpressure, Redis hot paths, read replicas, partitions, rate limits, and incident playbooks.

Production AI Readiness Matrix

The questions I ask before an AI system is allowed near real users.

Senior AI engineering is not just model integration. Production systems need grounded retrieval, bounded agent routes when tools are justified, measurable quality, rollback paths, and clear explanations when production gets loud.

Study the AI Systems Atlas

Grounding Can the answer prove where it came from?

Citations, chunk IDs, source metadata, reranking, and refusal behavior when evidence is weak.

Agent + Tool Safety Can agent actions be controlled and audited?

MCP schemas, RBAC, idempotency keys, dry-run paths, human review, and immutable audit trails.

Evaluation Can quality regressions be caught before users do?

Golden sets, grounding checks, latency/cost tracking, hallucination tests, and rollout gates.

Observability Can operators debug one bad answer end to end?

Request IDs, prompt traces, retrieved chunks, tool calls, model output, latency, cost, and feedback.

Latency + Cost Can the system stay fast without burning money?

Redis caching, async execution, batching, model tiering, token budgets, and retrieval pruning.

Reliability Can it survive retries, spikes, and partial failures?

Kafka queues, dead-letter paths, backpressure, replay-safe consumers, rate limits, and fallbacks.

Security Can it respect data boundaries?

Policy-aware retrieval, ACL filters, PII handling, tenant boundaries, redaction, and access logs.

Rollout Can changes be shipped and reversed safely?

Feature flags, canaries, prompt/index versioning, dashboards, alerting, and rollback playbooks.

Selected Work

AI case studies built like systems, not like resume bullets.

Some pages are passcode protected because they explain architecture patterns at a deeper level. Deeper architecture pages are available to employers and interviewers on request.

Non-confidential portfolio material

All diagrams and examples are generalized, recreated, and sanitized for public demonstration. They do not include proprietary code, internal documents, private data, or confidential implementation details.

Meta | Senior AI Engineer Internal AI Assistant Platform

Built an internal AI assistant platform with route-aware retrieval, safe tool access, bounded agent paths, async services, Redis caching, and cost-aware model execution.

Request Policy Route RAG/Tools Evals

35% lower latency 40% cost reduction 95%+ quality maintained

Owned: agent routing, retrieval, tool policy, async runtime, eval/cost improvements Proof: protected replay + route decision matrix

FAISS retrieval over permission-scoped knowledge sources.
MCP tools for policy-gated API and service access.
Async execution, Redis caching, token tracking, and automated evals.

View protected assistant platform case study

Loudegg | Lead AI Engineer Hybrid RAG and Agentic AI Platform

Architected a production route map that keeps simple requests on RAG, uses LangGraph for bounded tool workflows, and moves long-running coordination into async Kafka agents.

Docs Chunks Hybrid Search Agent Answer

50K+ documents RAG + agents Replayable workflow

Owned: end-to-end RAG/agent architecture and visual production workflow Proof: protected dry runs + clickable architecture flows

50K+ documents with deterministic chunking and versioned indexes.
Pinecone/OpenSearch retrieval with policy-driven routing.
LangGraph agent workflows, MCP tools, Langfuse/Datadog-style observability.

Open protected Loudegg visual system

Caesars Digital | Senior Software Engineer Trader AI Workflow and Real-Time Sports Platform

Built a trader-facing AI workflow and real-time sports data platform for odds, rules, investigations, and operational decision support.

Alert Odds Rules Agent Action

5M+ events/day Trader workflow Incident replay

Owned: agentic investigation workflow plus real-time event-system patterns Proof: protected incident replay + latency budget board

Hybrid RAG over historical odds, rules, and operational context.
LangGraph agents for complex trader investigations and workflow support.
Guardrails, audit logs, RBAC, cost controls, and 5M+ events/day pipelines.

View protected Caesars case study

Wells Fargo | Software Engineer Market Data System of Record

Built enterprise market-data infrastructure for pricing, risk, reconciliation, and compliance reporting.

Java Spring and Python services for market-data processing.
2M+ pricing records processed daily for FRTB/IMA risk computation.
Automated Hadoop pipelines that reduced reconciliation time.
Enabled faster compliance and risk reporting workflows.

Tata Consultancy Services | Software Engineer Microservices and Trade Validation

Modernized legacy systems into scalable backend services and high-throughput validation pipelines.

Spring Boot and Django microservices from legacy monoliths.
Kafka-based trade validation processing high-volume transaction flows.
Systems scaled for large concurrent user workloads.
Production reliability focus across enterprise clients.

Loudegg | Software Engineer Client Web, Mobile, and Cloud Applications

Built full-stack client products before the later AI platform work, covering web apps, mobile experiences, APIs, GraphQL, cloud deployment, and event-driven services.

React, JavaScript, TypeScript, Python, Node.js, and GraphQL product builds.
Microservices and Kafka-backed application workflows.
AWS, GCP, and Azure deployments for high-traffic client workloads.
End-to-end product execution from frontend experience to backend systems.

For Recruiters: Start Here

If you only have two to five minutes, scan this path first.

Start with the role fit, then scan the top three AI systems. Protected pages are available when an interviewer needs architecture depth.

Scan top work Request access

01 Scan AI systems work

Start with the Internal AI Assistant, Loudegg, and Caesars Digital.

02 Unlock architecture

Protected pages show routing choices, data movement, policy gates, and failure handling.

03 Open Atlas

Use the Atlas if you want a visual read on RAG, agents, queues, caching, and scale.

04 Reach out

Use email or the site chat to discuss protected details, interviews, or production AI roles.

Interviewer Decision Brief

Where I fit, what to review, and why the evidence is safe to share.

Use this as the quick read before a recruiter screen, hiring-manager review, or senior AI engineering interview loop.

Review work Open proof Start conversation

Best role fit Senior AI Engineer, Agentic AI Engineer, GenAI Platform Engineer, or RAG/Agent Systems Lead.

Strongest fit when the role needs architecture ownership plus hands-on delivery: agent routing, retrieval, tools, backend systems, evals, observability, cost controls, and rollout discipline.

Review first Internal AI Assistant Platform, Loudegg, and Caesars Digital.

Those three show the highest-signal GenAI, RAG, agent/tool-use, and distributed-system work.

Protected pages Architecture judgment, not hidden marketing copy.

They show route decisions, dry runs, guardrails, failure paths, and clickable system flows.

Safety boundary Sanitized and non-confidential.

No proprietary code, internal documents, private data, credentials, or confidential implementation details.

Start here Ask me how I move AI from demo to production.

Best topics: agent routing, grounding, tool safety, eval design, latency, cost, rollback paths, and operator visibility.

Protected AI Deep Dives

Public proof up front. Architecture rooms available when employers need the details.

The homepage gives a fast scan of scope, outcomes, and production judgment. The locked pages go deeper with route decisions, agent/tool dry runs, guardrails, failure paths, and clickable system flows.

Sanitized architecture only

Public outcome Protected architecture Animated dry runs

Reviewer path

Scan the card, open the case gate, then request access for the implementation-level walkthrough.

loudegg.system

Agentic AI + Hybrid RAG Platform

Protected

Documents become chunks, chunks become grounded answers, and hard requests route into bounded agent workflows.

Public: platform scope and outcomes Inside: ingestion, RAG, sync agents, async agents

Open Loudegg gate

assistant.platform

Internal AI Assistant

Protected

Enterprise routing across retrieval, tool policy, bounded agents, caching, evals, and cost controls.

Public: senior AI scope Inside: route matrix + safe execution replay

Open assistant gate

trader.workflow

Caesars Digital

Protected

Incident investigation agent plus realtime gaming/event pipeline thinking.

Public: platform and workflow scope Inside: incident replay + latency board

Open Caesars gate

Public Layer What I built

High-level outcomes, companies, technologies, and business impact stay visible to everyone.

Employer Layer How I built it

Architecture pages, workflows, diagrams, and system details require a passcode.

Signal Not a resume clone

The site behaves like a product: visual systems, dry runs, protected details, and proof.

Capabilities

The steps I take to make AI systems distinctly production-ready.

Strategy, data, design, development, launch, and growth are not separate worlds in real AI products. They are one loop, and every route has to be observable, testable, and safe.

[01] Strategy

Clarify the business workflow, risk level, users, latency budget, and where AI should not be used.

[02] Knowledge

Design ingestion, chunking, embeddings, sparse search, vector search, versioning, and replay.

[03] Experience

Make complex flows understandable with visual states, dry runs, citations, and clear failure paths.

[04] Development

Build typed services, agent state where needed, async jobs, tool layers, APIs, queues, caches, and deployment pipelines.

[05] Agent Control

Use planner-executor and parent-child patterns only when routing, state, tools, and guardrails justify them.

[06] Production

Add evals, observability, access control, rate limits, cost controls, rollbacks, and incident visibility.

[07] Growth

Measure answer quality, adoption, task success, model spend, latency, and workflow impact over time.

How I Think

My strongest work is designing the system around the LLM, not just calling it.

The portfolio is organized around production patterns: deterministic ingestion, hybrid retrieval, agent routers, planner-executor graphs, parent-child async agents, tool guardrails, cost-aware execution, and observability.

01 Knowledge Layer

Versioned document ingestion, chunking, embeddings, sparse/vector indexes, replay and rollback.

02 Routing Layer

Simple questions stay on RAG. Tool work enters bounded graphs. Long jobs become async workflows.

03 Agentic Control Layer

Planner-executor and parent-child patterns with max steps, typed state, idempotency, and safety gates.

04 Production Layer

OpenTelemetry, Langfuse-style traces, Datadog metrics, evals, feature flags, CI/CD, and rollout control.

AI Systems Atlas

A public systems atlas that explains agentic AI, RAG, MCP, Kafka, Redis, vector search, and scaling.

I added this page so reviewers can see how I think beyond one company or project: ingestion pipelines, agentic Jira workflows, context-window optimization, Kubernetes scaling, database indexing, read replicas, caching, queues, observability, and evals.

Open the visual systems lab

atlas.route.demo live systems map

chunks tool call trace

Docs ingest

Hybrid retrieve

Router RAG or Agent risk, cost, confidence

MCP tools

Ops evals

route RAG first, agent when useful safety policy gate before tools ops traces, evals, rollback

Scroll System Replay

Follow one AI request through the production path.

A cinematic version of the work behind the case studies: input arrives, policy checks it, retrieval grounds it, agents/tools handle bounded work, and observability keeps the answer traceable.

01 Request intake 02 Policy gate 03 Retrieval grounding 04 Agent/tool execution 05 Answer assembly 06 Trace + evaluation

Input User request intent, files, context

Gate Policy + auth scope, access, risk

Ground Hybrid retrieval chunks, rerank, citations

Act Agent + tools bounded steps, dry run

Output Grounded answer trace, cost, feedback

Ops Observability logs, evals, rollback

trace status request replay active

trace_id: az-prod-ai-042
route: rag -> policy -> tools
grounding: citations required
rollback: index + prompt versioned

Full-Stack Foundation

Earlier product work that made the AI systems stronger.

Kept lower on the page because the portfolio leads with production AI. This archive shows the full-stack foundation behind the AI work: web and mobile UX, auth, payments, maps, CRUD workflows, cloud deployment, and database-backed product experiences.

Business Websites Landing pages, service sites, and conversion-focused web presence

Designed and built sites for business owners who needed a more professional digital front door: clear service messaging, responsive layouts, calls to action, and deployment.

Booking and Operations Apps that turn business processes into usable workflows

Built appointment, reservation, listing, contact, and management-style apps with authentication, database-backed CRUD, maps, payments, and admin-facing workflows.

Mobile Experiences Mobile apps for events, notes, activities, and customer interaction

Delivered mobile-first and hybrid app experiences using Android, Ionic, Angular, Firebase, maps, camera/profile flows, and real-time data patterns.

Past Software Work

Client software and earlier product builds from the full-stack foundation.

This bottom archive stays below the AI case studies so the top of the portfolio remains focused on Agentic AI and GenAI. These projects show past client platforms and supporting software work: marketplaces, course sales, digital products, mobile apps, cloud apps, and tooling.

Client Software Dr Bees List

Dental appointment marketplace connecting patients, verified dentists, and platform admins, with onboarding, booking conflict prevention, Stripe payments, reviews, notifications, search, and admin operations.

GitHub repo Booking flow video Workflow demo video

Client Course Platform NYTTO

Online course-selling platform for AACCT, a table tennis agency, focused on packaging training content into a clear public course workflow.

Course platform video

Client Product Platform Systems and Forecast

Digital product-selling platform for packaging, presenting, and selling downloadable or online products through a clear customer-facing purchase workflow.

Product platform video Sales workflow video

AI Agent Lab Invoice Processing Manager Agent

A manager-style workflow orchestration demo for document extraction, validation, formatting, and human-review patterns.

GitHub repo

Client Booking Platform NYTTF

Table-tennis booking marketplace where players search and reserve tables from facility owners, with provider approval, real-time availability, Stripe payments, chat, reviews, notifications, search, admin governance, and analytics.

Android Remember Book

Native Android notes app using Java, XML, Material Design, SQL persistence, content providers, loaders, and durable CRUD behavior.

GitHub repo

Learning UX CodeHub

Coding-problem website built with HTML, CSS, and JavaScript, focused on making algorithmic problems more understandable through layout and animation.

GitHub repo

Developer Tooling GitKick CLI

Node.js command-line tool that automates git initialization, remote repo setup, `.gitignore` creation, and first push through an interactive wizard.

GitHub repo

Cloud App Hotel Radisson

Hotel room reservation platform with Angular, Spring Boot, reactive MongoDB, REST APIs, and user-facing booking workflows.

GitHub repo

Technical Range

AI engineering plus backend, distributed systems, and cloud execution.

Python FastAPI LangChain LangGraph Agentic AI AI Agents RAG Vector DBs FAISS Pinecone OpenSearch MCP Kafka Redis AWS Kubernetes Docker OpenTelemetry Datadog React

Beyond Engineering

The same mindset shows up outside code: discipline, systems thinking, and long-game execution.

Table Tennis Competitive table tennis player

Competitive table tennis has shaped how I practice: fast feedback loops, pattern recognition, pressure control, and thousands of tiny improvements that compound.

Systems Long-term systems thinker

I’m interested in durable systems, incentives, ownership, and long-horizon decision-making. That lens shapes how I think about engineering tradeoffs and resilient product design.

Contact

Want to review the protected architecture pages or talk production AI?

Email is the best way to reach me for senior AI engineering roles, GenAI and agentic platform work, architecture interviews, or access to protected case-study details.

Email abrarzahin@yahoo.com

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