Abdul W.

I build production AI systems that automate expensive business workflows such as sales research, operations, document processing, knowledge retrieval, customer workflows, and internal decision support. If your team is manually moving information between documents, databases, CRMs, APIs, spreadsheets, or internal tools, I can design and deploy an AI workflow that handles much of that work reliably while keeping humans in control where judgment matters. I'm an AI/ML engineer with 6+ years of experience and 70+ clients served. I work across AI automation, AI integration, agents, RAG, backend engineering, and ML infrastructure — from identifying the workflow worth automating through production deployment and handoff. WHAT I CAN BUILD FOR YOU AI Automation & Workflow Integration I turn multi-step business processes into automated AI workflows. This can include researching information, extracting and validating data, calling APIs, updating databases or CRMs, generating structured outputs, routing tasks, and escalating exceptions for human review. AI Agents & Agentic Systems I build agents using LangGraph, LangChain, tool calling, structured outputs, and custom Python/TypeScript services. Agents can reason across multiple steps, use business tools and APIs, maintain state, apply approval rules, and coordinate specialized sub-agents. I focus on production behavior rather than agent demos: retries, fallbacks, validation, audit logs, human approval points, cost controls, observability, and predictable outputs. AI Integration for Existing Products You do not always need a new AI product. Often the highest ROI comes from adding AI to software you already use. I integrate OpenAI, Anthropic, Llama, Qwen, Mistral and other models into existing SaaS products, internal applications, databases, APIs and operational workflows. RAG, Knowledge Systems & Document Intelligence I build systems that turn company documents and unstructured information into usable operational knowledge. This includes: • RAG with citations and source provenance • hybrid vector and keyword retrieval • Graph RAG with Neo4j • OCR for scanned documents • structured extraction from PDFs • entity and relationship extraction • multi-hop retrieval • natural-language querying over company data The goal is not simply to “chat with PDFs.” I build systems that retrieve trustworthy information and use it inside real workflows. Natural Language to Data I build agents that let teams ask questions about PostgreSQL, MySQL and structured business data in plain English. The system translates requests into controlled queries, retrieves the data, and converts results into useful explanations or actions. Production AI Backends I build FastAPI services for AI applications with authentication, async processing, queues, streaming, rate limits, caching, logging, Docker deployment and cloud infrastructure. For heavier workloads I also work with GPU inference, PyTorch, Hugging Face, distributed training and model optimization. RECENT SYSTEMS Graph RAG Knowledge Assistant Built an agentic knowledge system using Neo4j, vector retrieval, LangGraph, OCR and FastAPI. It processes documents, preserves relationships and provenance, routes questions through specialized retrieval paths and streams evidence-grounded answers. Content Evaluation & Rewriting Pipeline Built a multi-agent LangGraph workflow that evaluates content across multiple dimensions, performs structured analysis, plans improvements and executes controlled rewrites with traceable intermediate steps. Natural Language to SQL Agent Built an AI agent that converts business questions into SQL, queries PostgreSQL/MySQL and returns understandable results while separating language-model reasoning from database execution. GPU-Accelerated Processing API Built a production FastAPI system on AWS with S3, Celery, Redis and NVIDIA GPU processing for asynchronous workloads. HOW I APPROACH AI PROJECTS I start with the business workflow rather than the model. 1. Identify the manual process, bottleneck and success metric. 2. Design where AI should act and where deterministic logic or human approval is safer. 3. Ship a working vertical slice quickly. 4. Measure accuracy, latency, cost and failure cases. 5. Add monitoring, retries, security and operational controls. 6. Deploy and document the system so your team can own it. I can also work in a forward-deployed engineering role when the problem is not fully specified — working directly with your team to understand the workflow, prototype solutions, integrate with existing systems and iterate until the automation produces measurable value. Core stack: Python · TypeScript · FastAPI · LangGraph · LangChain · OpenAI · Anthropic · Llama · Qwen · Neo4j · PostgreSQL · MySQL · FAISS · ChromaDB · Redis · Celery · Docker · AWS · Azure · Runpod · PyTorch · Hugging Face Share your workflow or AI bottleneck. I’ll identify what to automate and build it reliably.