Hire the Best Machine Learning Engineers
in Australia

I build AI that works in production. Not demos. Two things I do most. Custom ML and AI builds (scoring models, NLP, RAG, GPT-4 and Claude integrations). And making operational tools smart with automation and AI agents. ClickUp is where I do a lot of this, but the same approach works for any workflow tool your team lives in. I mostly work with startups and small-to-mid sized teams (10-50 people) who have outgrown spreadsheets and manual processes but are not ready to hire a full data team. A couple of examples. A 35-person marketing agency hired me to fix their ClickUp. I rebuilt the whole workspace. New structure, automated handoffs across six departments, AI briefing summaries. Saved them 15+ hours a week and cut handoff mistakes by more than half. For another client I built a RAG system on top of their internal docs so their support team could answer customer questions in seconds instead of digging through Notion for 20 minutes. Stack I use most: Python, scikit-learn, XGBoost, spaCy, HuggingFace, OpenAI, Anthropic, Streamlit. Cursor and Claude every day for shipping faster. Next.js, Postgres, Make, n8n when the project needs it. I document everything and hand it off properly. Nothing I build falls apart when I leave. Clean code, real docs, support after delivery so your team owns it. If any of this sounds like what you need, send me a message and tell me what you are trying to fix. I will tell you honestly if I am the right person for it.

I am a machine learning engineer with a strong interests in reinforcement learning, biotechnology, and bioinformatics. I have experience in deep learning, computer vision, and AI model development, in particular, creating models to analyse medical images to detect diseases and process single-cell RNA sequencing (scRNA-seq) data for biological insights. I hold a bachelors degree in biotechnology minoring in bioinformatics and a masters degree in data science. My expertise includes: - Reinforcement Learning algorithms to play video games - Computer Vision & Deep Learning (CNNs, Transformers) - Bioinformatics & Computational Biology (sequence analysis, protein structure prediction) - Data Science & Predictive Analytics (statistical modeling, data visualization) - Python, TensorFlow, PyTorch, OpenAI Gym, Scikit-learn

I'm Ghulam Nabi, A self-motivated and confident Machine Learning engineer and Data Scientist who has extensive knowledge in Machine Learning Techniques, Computer Vision, Python. In addition to that, can apply deep learning algorithms to real-world applications and work with data visualization and storytelling. I have more than 3 years of experience as a Data Scientist and have more than 2 years of experience as a freelancer on Upwork. Other than that I am a full-fledged researcher who can find novel solutions to many machine learning, computer vision, and data science problems. This is evident for my peer-reviewed Journal and conference papers which you can find on my google scholar profile by searching the name "Ghulam Nabi Ahmad Hassan Yar". The skills I possess make me a good combination of a developer and a researcher. Due to this, I'm good at both developing and creating documentation. I really shine in the R&D which combines both my skills. A flexible, enthusiastic & hardworking person with excellent software, interpersonal, communication, and time management skills seeking work to meet my competencies, capabilities, skills, education, and experience.

I have shipped 𝟐𝟎+ 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐀𝐈/𝐌𝐋 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 — LLM applications, RAG pipelines, AI agents, and voice AI — for SaaS startups, product teams, and enterprise businesses, backed by 7+ years of engineering experience and a 5.0 rating across every completed project. If you need more than a prototype, I can take your AI product from architecture and model selection to API integration, evaluation, cloud deployment, observability, and scaling. I’m Ali H., a Senior AI/ML and LLM Engineer with 7+ years of experience across Generative AI, machine learning, NLP, backend engineering, data pipelines, and cloud AI infrastructure. I build reliable AI systems that work with real business data, tools, APIs, and users. 🧠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 ✔ AI agents using LangGraph, LangChain, CrewAI, and AutoGen ✔ Tool calling, planning, memory, routing, and human approval workflows ✔ Multi-agent systems connected to APIs, databases, CRMs, and SaaS tools ✔ AI workflow automation using n8n, Python, webhooks, and API integrations ✔ Automated research, support, document processing, reporting, and lead workflows 📚 𝐑𝐀𝐆 & 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 ✔ Retrieval-Augmented Generation pipelines for PDFs, websites, databases, and cloud storage ✔ Embeddings, semantic search, hybrid retrieval, reranking, and metadata filtering ✔ Vector databases including Pinecone, Weaviate, FAISS, ChromaDB, and pgvector ✔ Grounded answers, source citations, hallucination reduction, and RAG evaluation ✔ Enterprise knowledge assistants, document AI, and intelligent search systems 🤖 𝐋𝐋𝐌, 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 & 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 ✔ OpenAI, Claude, Gemini, Llama, Mistral, Mixtral, and Hugging Face integration ✔ Prompt engineering, structured outputs, function calling, and context optimization ✔ LLM fine-tuning using LoRA, QLoRA, PEFT, and domain-specific datasets ✔ Synthetic data generation, model evaluation, benchmarking, and guardrails ✔ Quantization and optimization using GGUF, GPTQ, and AWQ ✔ AI copilots, chatbots, assistants, and Generative AI SaaS features 📊 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐍𝐋𝐏 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 ✔ NLP pipelines for classification, extraction, summarization, and semantic analysis ✔ Model training, validation, evaluation, and production inference workflows ✔ Data preprocessing, feature pipelines, experiment tracking, and model monitoring ✔ Custom machine learning APIs and AI-powered decision systems ✔ Integration of ML models into SaaS products and enterprise applications 🎙 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 & 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 ✔ AI voice agents for inbound and outbound calls ✔ Deepgram, ElevenLabs, Twilio, Amazon Polly, and real-time voice integrations ✔ Appointment booking, lead qualification, call routing, transfers, and summaries ✔ Voice agents connected to CRMs, calendars, databases, and internal tools ⚙️ 𝐀𝐈 𝐀𝐏𝐈𝐬, 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ✔ Python, FastAPI, Flask, Django, Node.js, and REST API development ✔ vLLM, TGI, DeepSpeed, and open-source LLM deployment ✔ AWS Bedrock, AWS, GCP, Azure, RunPod, Docker, Kubernetes, and CI/CD ✔ GPU inference optimization, batching, caching, monitoring, and cost control ✔ Secure authentication, logging, error handling, guardrails, and observability 𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝: ✔ AI agents and multi-agent systems ✔ RAG chatbots and enterprise knowledge assistants ✔ AI voice agents and conversational AI ✔ Generative AI and machine learning applications ✔ LLM fine-tuning and open-source model deployment ✔ Document AI and intelligent data extraction ✔ AI workflow automation with n8n and APIs ✔ Scalable AI SaaS products and inference systems I focus on production reliability, response accuracy, low latency, security, maintainability, and cost-efficient scaling—not just demos. Core Expertise: AI Engineer, Machine Learning Engineer, LLM Engineer, Generative AI Developer, AI Agent Developer, RAG Developer, AI Chatbot Developer, Voice AI Developer, LangChain, LangGraph, CrewAI, AutoGen, OpenAI API, Claude API, Gemini API, Hugging Face, Vector Database, Pinecone, pgvector, FastAPI, LoRA, QLoRA, AWS Bedrock, vLLM, AI Automation, n8n, Multi-Agent Systems

I build production AI systems — not demos, not prototypes. If you need an AI engineer who can take a product from architecture through deployment, here's what I bring: → AI Agents & LLM Systems: Built end-to-end agentic workflows integrating OpenAI, Gemini, and ElevenLabs with multi-modal data pipelines (text, audio, images) at scale on GCP. Experience with RAG architectures, tool-calling, memory systems, and automation pipelines. → Backend & APIs: FastAPI and Flask in production. Supabase/PostgreSQL schema design that reduced query latency by 40%. Deployment via GCP with CI/CD from GitHub. → ML Engineering: 4+ years delivering production models — cancer segmentation at 98% accuracy, real-time flood monitoring, multilingual speech-to-text across 4 languages, anomaly detection. PyTorch, TensorFlow, HuggingFace, scikit-learn. → Document Intelligence: RAG pipelines, automated document summarisation, custom GPT systems for domain-specific terminology (70% reduction in processing time for one client). I've maintained a 100% Job Success Score across 4+ years as a Top Rated freelancer. I work best with clients who have a real product problem and need an engineer who can own the AI layer end-to-end. Available 20+ hrs/week. Sydney, Australia (AEST)

I’m a PhD Candidate and AI Engineer specializing in Large Language Models (LLMs), AI Agents, and Distributed Machine Learning Systems — with experience spanning both research and production-level AI engineering. My work bridges deep technical R&D with practical system design, enabling organizations to scale intelligent automation and reasoning systems efficiently. At Velaris, I engineered and optimized large-scale AI pipelines for their Customer Success platform. I built the Transcript Agent, an asynchronous AWS-based pipeline (S3, SQS, Lambda, FastAPI) for automated meeting transcript analysis — cutting cloud compute costs by 60% through advanced preprocessing and dynamic batching. I also led the design of Pandula AI and Fact Factory, taxonomy-driven summarization frameworks integrating LLMs, topic modeling (LDA), and event-driven consumers, improving throughput, accuracy, and interpretability across customer data workflows. As the Founder and Lead ML Engineer at Synacal, I’ve designed and deployed RAG (Retrieval-Augmented Generation) and multi-agent systems using LangChain, AutoGen, CrewAI, and LangGraph, powering AI-driven automation for finance, marketing, and analytics platforms. My focus is on building scalable, cost-efficient, and explainable AI ecosystems that integrate seamlessly with enterprise infrastructures. My research at Deakin University (Australia) and Coventry University (UK) explores Explainable and Trust-Aware AI, combining Deep Reinforcement Learning, Knowledge Graphs, and Cultural Emotion Modeling to enhance reasoning and human–AI collaboration. My frameworks, including CARA and Affective-CARA, have achieved measurable improvements such as a 61% reduction in cultural bias and state-of-the-art performance in LLM reasoning tasks. With a strong command of Python, PyTorch, FastAPI, LangChain, AWS, Bedrock (Claude, Llama), Docker, Kubernetes, PostgreSQL, and Kafka, I excel at designing systems that are both scientifically grounded and production-ready. Whether it’s building decentralized AI architectures, optimizing LLM pipelines, or developing intelligent agents — I aim to deliver robust, transparent, and scalable solutions that push the boundaries of modern AI. If you’re working on cutting-edge AI research or need an expert to build intelligent, distributed, or agentic systems, I’d love to collaborate and help you bring your vision to life.