Hire the Best Certified AWS Machine Learning Engineers

Hello, I am investing all my time and resources in Upwork ☝ My experience covers data analysis, AI/ML model training, fine-tuning, and deployment to production on AWS, GCP, Azure, or edge devices. ⬣ Skills : GenAI : RAG, Vector databases, LLM finetune, AI Agent/Multi Agent systems. Machine Learning : classification, regression, similarity search. Computer vision : object detection&tracking, pose estimation, image processing. ⬣Programming languages : Python, MATLAB,C#. ⬣ ML/DL LIBRARIES : TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, OpenCV,Pytorch, HuggingFace,Unsloth, Ultralytics. ⬣ Inference engines: llama.cpp, OLlama, LiteRT-LM, TensorRT. ⬣ Certificates : ✅ AWS Certified Solutions Architect Professional ✅DeepLearning.AI Machine Learning Engineer for production I AM READY TO IMPLEMENT YOUR PROJECT AND CONVERT YOUR IDEAS INTO A REALITY!

I build production LLM systems for industries where a wrong answer costs real money — insurance, finance, law, property. Expert-Vetted (the badge Upwork reserves for its top 1%), 100% Job Success across 70+ projects and six years. Not prototypes. Not proof-of-concepts. Production systems that non-technical people depend on every day. WHAT I DO Two specialisms: 1. Document intelligence — extracting structured, validated data from messy real-world documents (scanned PDFs, hand-filled forms, appraisals, payslips, 200-page policy manuals) at accuracy a business can put its name to. 2. Agentic LLM systems — multi-agent and multi-step workflows where AI makes real decisions: schema-validated, cross-checked by a second model, observable at every step, with a human in the right place by design. The common thread: a manual process that took days becomes an automated pipeline that takes minutes — with the evidence to prove it didn't get worse. THE RECORD • Lead engineer on a production AI underwriting platform, sold through a policy-software vendor serving 18 insurance carriers: 99.55% precision on discount and surcharge rules, 96.14% overall — policy review went from days to 3–5 minutes • 12M+ UK planning documents (120M+ pages) extracted and embedded in under 48 hours, at 65–75% below the GPT-4 baseline cost • Payslip extraction for a UK mortgage broker: 52% fewer processing errors, 80% less manual review, 15% faster mortgage approvals • 700+ UK Local Plans summarised weekly, fully automated, at 75% lower LLM cost through context caching and batch APIs • A consumer-fintech assistant where every financial figure is computed by deterministic code, not the model — zero made-up numbers by construction • A private-equity deal-screening agent that completes the firm's own investment scorecard end-to-end, backtested against realised fund returns The 99% figures are maintained properties, not launch statistics: golden-sample regression suites replay real production cases on every change. HOW I WORK I default to the simplest implementation that works and iterate with evidence. Strong prompting before fine-tuning; an API call before custom infrastructure; a benchmark before an architecture decision. Non-negotiables on every pipeline: • Pydantic schema enforcement — if it doesn't validate, it doesn't pass • Dual-model verification for high-stakes data • Full observability (LangSmith / Langfuse) — nothing is a black box • A gold-standard test set built early, regression-tested on every change Communication: daily written async updates and a weekly Loom walkthrough. WHAT CLIENTS SAY "His Loom updates were the highlight of my week! He's great at communicating complex AI concepts to non-technical people and keeps you in the loop every step of the way." — Chris Barnes, Co-Founder, Gains App "Adam is an absolute powerhouse of an LLM Engineer. He has first class communication skills which make working with him an absolute pleasure." — Sammie Ellard-King, Founder, Gains App "A great balance of personality, professionalism and a deep knowledge of the AI space. He's a strategic thinker who considers the bigger picture." — Tom Story, PlannrAI TECHNICAL STACK • Orchestration: LangChain, LangGraph, LangSmith, Langfuse, Claude Code / agent skills, MCP • Models: Claude, GPT, Gemini, Llama — via Bedrock, Vertex and direct APIs • Infrastructure: AWS (ML Specialty certified), Docker, Kubernetes, Postgres + pgvector, FastAPI • Core ML: Python, PyTorch, pandas, scikit-learn • Validation: Pydantic, structured outputs, dual-LLM verification, LLM-as-judge test suites CURRENTLY TAKING ON • Enterprise document processing — financial, insurance, legal, mortgage, property • Agentic workflows that have to be right, not just impressive • LLM cost and accuracy work on existing systems • Production pipeline architecture and technical leadership Expert-Vetted · 100% Job Success · $400K+ earned · 5,750+ hours · 70+ projects

🚀 🥇 Expert-Vetted Talent | Principal AI/ML | GenAI, LLM, RAG, Fine-tuning, Cloud(Azure, AWS, GCP) | Healthcare & FinTech I transform complex business challenges into cutting-edge AI solutions. With 15+ years leading AI/ML initiatives and a Doctor of Philosophy in Machine Learning from Iowa State University, Master's in Computational Neuroscience from UC San Diego, I deliver production-ready GenAI systems that drive measurable ROI. ✅ What I Offer: • GenAI & LLM Solutions: Custom GPT fine-tuning, prompt engineering, multi-agent systems, RAG architectures • Production AI Systems: End-to-end ML pipelines, MLOps, model deployment at enterprise scale • Healthcare AI: Clinical trial automation, medical document generation, HIPAA-compliant solutions • AI Strategy & Architecture: Technical roadmapping, proof-of-concepts, AI transformation leadership • Full-Stack AI Development: Python, PyTorch, TensorFlow, AWS, GCP, Azure, Vector DBs, Kubernetes 🎯 Recent Achievements: • Built AI system processing 100K+ clinical trials for pharma enterprise, enabling intelligent Q&A and benchmarking • Developed GenAI platform generating 100+ page clinical trial protocols with 95% accuracy • Led 60+ engineer team delivering first commercial AI healthcare product at ResMed (NYSE: RMD) • Created $1M+ revenue AI products in first year at multiple startups • Secured $3.5M+ funding through strategic AI product development 💼 Industry Expertise: • Healthcare/Pharma: Clinical trials, EHR integration, medical AI, FDA-regulated software • FinTech: Real-time fraud detection, risk assessment, payment processing systems • Enterprise SaaS: Multi-tenant architectures, API development, scalable AI services • Retail/E-commerce: Recommendation engines, inventory optimization, customer analytics 🔧 Technical Stack: AI/ML: GPT-4/5, Claude, Llama, BERT, Transformers, Fine-tuning, RLHF, RAG, Vector Databases Languages: Python, JavaScript, TypeScript, Java, Ruby, Go, SQL Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain, Scikit-learn, FastAPI Cloud/DevOps: AWS (SageMaker, Lambda, ECS), GCP (Vertex AI), Azure, Kubernetes, Docker Databases: PostgreSQL, MongoDB, Redis, Elasticsearch, Pinecone, Weaviate, ChromaDB Tools: MLflow, Weights & Biases, Git, CI/CD, Jupyter, Streamlit 📊 Quantifiable Impact: • 73% engagement increase for nonprofit youth chatbot with emotional intelligence • 94% accuracy in crisis detection for at-risk populations • 10X growth in subscriber base through AI-driven optimizations • 100+ pages of medical documents generated with regulatory compliance • $100K+/month revenue generated through ML-powered ad targeting 🎓 Credentials: • M.Sci. Computational Neuroscience - UC San Diego • IBM Certified: RAG and Agentic AI • Deep Learning Specialization - Coursera • Published researcher in AI/ML (Psychological Science, ICSE) • 4 provisional patents in AI/computer vision 🌟 What Sets Me Apart: Unlike typical developers, I combine deep technical expertise with business acumen from leading multiple successful startups. I don't just build models – I architect complete AI ecosystems that scale, comply with regulations, and deliver measurable business value. My neuroscience background provides unique insights into building truly intelligent systems that understand human behavior and needs. 📋 Services I Provide: • Custom LLM fine-tuning and deployment • RAG system architecture and implementation • Multi-agent AI system development • Healthcare AI and medical document automation • AI-powered data extraction and processing • Computer vision and image analysis solutions • Production ML pipeline development • AI strategy consulting and roadmapping • Technical due diligence for AI projects • Team mentoring and AI capability building 🤝 Working With Me: I pride myself on clear communication, translating complex AI concepts into actionable business strategies. Whether you need a production-ready AI system, strategic guidance, or technical leadership, I deliver solutions that exceed expectations. I'm available for both short-term projects and long-term engagements. Ready to transform your business with AI? Let's discuss how I can help you leverage the latest in GenAI, LLMs, and machine learning to achieve your goals. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, GenAI, Generative AI, ChatGPT, GPT-4, GPT-5, Large Language Models, LLM, Fine-tuning, RAG, Retrieval Augmented Generation, RLHF, Vector Database, Embeddings, Transformers, NLP, Computer Vision, PyTorch, TensorFlow, Python, AWS, GCP, Azure, Healthcare AI, FinTech AI, MLOps, AI Architecture, Prompt Engineering, LangChain, Hugging Face

2X GCP Certified • Google Certified Proffessional Machine Learning Engineer • Google Certified Associate Cloud Engineer Contributed to Stanford Research (Echonet Dynamic) open source project for Interpretable AI for beat-to-beat cardiac function assessment. Successfully completed Stanford online certification in Machine learning with 97.3% grade. I love to work on challenging and state of the art cutting edge machine learning and artificial intelligence projects that push the edge or require the latest research in the field of machine learning and artificial intelligence. I have worked on attention mechanisms for improving the efficiency of existing models as well as creating new models from scratch that increase the capabilities for the model to solve the particular task much more effectively. I am able to implement "pytorch" "tensorflow" "keras" "octave" "tflearn" "sklearn" "pandas" "matplotlib" "nunmpy" "scipy" among others for any machine learning tasks, in the wide range of application spectrum. I am able to design front end back-end of the apps or a website etc. Can work on web-sockets servers google cloud platform hadoop reactjs image processing end to end models (including data prepossessing) I have successfully concluded a number of competition on kaggle with respectable positions on the leader-board. I am able to implement bhednau and Luong Attention mechanisms both in image (or video) classification, object detection or surveillance tasks, as well as in NLP (Natural Language Processing). NLP (implementation include:) Spacy Glove Vector Custom embeddings etc Can work with: Python reactjs c++ bash linux octave AWS Google cloud Terraform Ci/CD pipelines Kubernetes Tools: Prometheus Grafana Docker google cloud ml google ai-platform aws sagemaker google ml toolkit Azure Databricks Google AutomL Apart from the above interest, I also have always had the aspiration of being a writer.Starting of from the minor projects I am on my way to write a book, a semi-fictional psychoanalysis.I would love to write about new things and therefore add on to my own knowledge while doing that and earning at the very same time.

🎯 100% Refund If I Set the Wrong Expectations and Can’t Deliver. No Questions Asked. ⚙️ Need AI automations for lead generation, outbound sales, CRM workflows, appointment setting, customer support, email automation, and scalable business operations using n8n, Make, Claude, and Python? Consider it done! 🧠 I’ve built AI agents that not only perform tasks, but learn to improve themselves within your system turning AI automation into intelligent adaptation. ⚙️ Little bit more about me: I’m an AI Automation specialist & Data Analytics Engineer with over 10 years of experience in machine learning, Python,Django, FlaskAPI and workflow automation using tools like n8n, Zapier Claude and make. I specialize in building scalable, AI-driven solutions that solve real-world problems and turn data into actionable insights. From predictive analytics to real-time dashboards. I help organizations automate processes and rapidly build MVPs to scale. My goal is to empower businesses by providing intelligent, data-driven solutions that evolve with their needs. ✅ Proven track record with 150+ AI and automation projects, driving efficiency and growth ✅ Expert in AI, data analytics, and machine learning with 10+ years of experience in Python, Django ✅ Certified Zapier Expert, skilled in Make, n8n, and Airtable for seamless integrations ✅ Specializing in real-time data solutions and predictive models for faster decision-making ✅ Delivering high-impact solutions with clear communication and scalable results ✅ Focused on providing measurable ROI through custom automation and AI solutions If you are looking for a Top-rated professional, who is in the list of Top 3% of Upwork profiles and fast and efficient delivery, then I'm your man. What I Offer: N8N | Zapier | Make | Replit | Python | AI | Machine Learning | AI Automation | Predictive Modeling | TensorFlow | PyTorch | Time-Series Forecasting | Recommendation Engines | Deep Learning | Generative AI | LLM | Chatbots | RAG | MVP Prototyping | Streamlit | Dash | Tableau | Power BI | Data Visualization | KPI Tracking | Trend Analysis | Performance Monitoring | Data Engineering | ETL ELT Pipelines | Web Scraping | MongoDB | SQL | Redis | FastAPI | Cloud Services | AWS | Automation | Django | ROI Optimization | Product Validation | MVP 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲: - 10+ years of professional Python development - Fast communication & on-time delivery - Flexible, reliable, and high-quality solutions - Budget-friendly and negotiable pricing - 100% client satisfaction is always my priority - Available for one-time tasks or long-term collaborations

Upwork rates: Pay band: $80 for a consultation session (two hours maximum) Hourly: $70-120 an hour Outcome based: $200/feature Need an ML system that actually works in production, not just a notebook demo? I build AI solutions that ship. My systems process 50,000+ requests daily with sub-200ms latency. I take projects from research prototype to deployed product, handling the messy middle that most ML engineers avoid. What I deliver for clients: ### Voice AI Agents I build voice AI agents and conversational pipelines that work the way real users actually talk, which is messy, interrupted, and unpredictable. I use Bland.ai to design and deploy production voice agents with reliable call handling, dynamic routing, and tool-calling backends that connect to your existing systems. At AskHumans, I built outbound and inbound call agents for lead qualification, appointment booking, and live customer support. I wired the agent into the client's stack via Calendly webhooks for live scheduling, then reviewed transcripts post-launch to find where calls were dropping or going sideways, retuning the prompt and flow logic until call satisfaction hit 96.5%. ### LLM Applications and RAG Systems I build retrieval-augmented generation pipelines, multi-agent systems, and LLM-powered tools using LangChain, LangGraph, and direct API integrations. Recent work includes a document processing system that cut manual review time by 70% and a synthetic data pipeline generating 100K+ training samples daily. ### Production Machine Learning Your model is only valuable if it runs reliably at scale. I handle the full stack: model optimization, API development with FastAPI, containerization with Docker and Kubernetes, and cloud deployment on AWS (SageMaker, Bedrock, Lambda, EC2). I have built systems that maintained 99.9% uptime while processing enterprise workloads. ### GPU Optimization and High-Performance Computing Slow inference kills user experience. I optimize models with CUDA, implement efficient batching strategies, and architect distributed computing solutions. Background includes work on HPC clusters at NIH and CMU research computing. ### Graph-Based ML and Knowledge Systems When your problem involves relationships and networks, I bring specialized experience in graph neural networks, knowledge graphs, and Neo4j. I have applied these techniques to drug discovery research, fraud detection, and recommendation systems. My background spans research and industry. Three years of ML research at NIH and Carnegie Mellon gave me strong fundamentals in experimental design and statistical rigor. Current work as an AI/ML engineer at a federal consulting firm taught me how to build systems that meet enterprise requirements for security, compliance, and scale. I communicate clearly and keep you updated without requiring you to manage me. You will get working code, documentation, and systems you can maintain after the engagement ends. If your project involves voice agents, large language models, machine learning infrastructure, or getting an AI system into production, send me a message with the details. I will tell you honestly whether I am the right fit. Looking forward to hearing from you. ### Skills section Voice AI & Conversational Agents: Bland.ai, webhook integrations (Calendly, CRMs), transcript analysis, call flow optimization LLM Research & Fine-Tuning: HuggingFace Transformers, domain-specific fine-tuning, RAG systems, AWS Bedrock, OpenAI & Anthropic APIs, vector databases (Pinecone, Weaviate) Multi-Agent Systems: LangChain, LangGraph, agentic NLP pipelines, graph-based coordination frameworks, multi-agent orchestration Machine Learning & Deep Learning: PyTorch, TensorFlow, Graph Neural Networks (GraphSAGE), GANs, VAEs, synthetic data generation, reinforcement learning, computer vision, statistical learning theory High-Performance Computing & GPU Optimization: CUDA programming, GPU optimization, GraphBLAS, distributed computing, HPC clusters (SLURM), parallel algorithms, multi-node training Cloud & MLOps: AWS (SageMaker, Lambda, S3, EC2, Bedrock, CloudWatch), Docker, Terraform, Kubernetes, CI/CD, infrastructure as code, model deployment Full-Stack Development: Python, JavaScript/TypeScript, React, Next.js, FastAPI, Flask, Node.js, REST APIs Data Engineering: PostgreSQL, MongoDB, Neo4j, Pinecone, Weaviate, ETL pipelines, data visualization Cheminformatics: RDKit, molecular property prediction, graph-based drug discovery, synthesis route planning, SMILES encoding, Tanimoto similarity, NetworkX Other: Secret clearance (TS/SCI in progress), NVIDIA Accelerated Computing Certified, C/C++, Git