Hire the Best Artificial Intelligence Engineers

I manage a team of smart Fullstack Engineers, DevOps, Product managers, and consultants, product analytics experts. I individually worked on 50+ projects. ✅ AI/ML development: Computer Vision AI, ChatGPT, LLM, Stable-diffusion, Python, HuggingFace, Deep Learning, ML Predictive Modeling, OCR & more. ✅ WebRTC & Jitsi Video conferencing application. ✅ RTMP live streaming. ✅ Product and Business Development Manager mostly worked for B2B SaaS products. ✅ Expertise in User-centered designs, MVPs, Devops, Wireframes, and visual designs with Figma. ✅ Expertise in Laravel, Strapi, MERN stack ✅ DevOps monitoring and configuration tools ✅ WordPress, plugin development web config setups with troubleshooting and LEMP stack.

Most developers talk about "sprints" that take weeks. I talk about "deliveries" that take days. I am a New York based software engineer and creative technologist. I have spent 9 years bridging the gap between complex logic and intuitive design. I don't just "write code" I build scalable market advantages. My clients call me the Amazon Prime of Development because I prioritize two things above all else "extreme speed and precision". I specialize in rapid prototyping. I will have a functional prototype of your vision ready in less than 72 hours. And full scale production ready deployment usually within 7 days. Why work with me? While most engineers get stuck in the "system" I think in "strategy". Whether it's a trading bot in Rust, a DeFi protocol on Solana, or an AI driven healthcare platform. I focus on the ROI of the build. I lead a collective of elite developers, allowing us to work in parallel to execute complex architectures (Web3, Robotics, IoT) faster than a traditional agency ever could. Core Skills: 1. Web3 & Blockchain - Solana, Ethereum, Rust, Go, Smart Contract Auditing. 2. Artificial Intelligence - Custom LLMs, n8n automation, NLP, Computer Vision. 3. Full Stack - React, Node.js, Python, AWS Infrastructure. 4. Specialized Industries - FinTech, HealthTech, IoT, Robotics. Lastly, I don’t just meet expectations, I break them. If you have a big idea and need it live by next week, let’s talk. Portfolio: amarifieldsvisions.com

🌟 Message me if you're interested in automating your business using AI/ML tools! I can help support and streamline your decision-making processes. 💡 I'm a ML engineer with relevant experience of AI integration into business. Here’s a core set of services I offer: 🔸 Agent end-to-end development and deployment with both low and high code frameworks. 🔸 WhatsApp/Telegram chatbots, with RAG, grounding architecture, using vector databases like Pinecone or ChromaDB. 🔸 LLM integration, using public APIs like OpenAI, VertexAI, AWS SageMaker, or Claude, as well as domain-specific fine-tuning. 🔸 Data collection and ETL using tools like Apify, Selenium, BeautifulSoup, requests. 💻 Tech Stack: Programming languages: Python Agent development: OpenAI, VertexAI, AWS SageMaker, Claude APIs, Pinecone, ChromaDB, LangChain, LangGraph, Hugging Face Transformers, Ollama Agent monitoring: LangFuse, LangSmith Deep Learning/ML: PyTorch, TensorFlow/Keras, scikit-learn, pandas, matplotlib/seaborn, NumPy Cloud services: AWS, GCP/GCS, Snowflake Databases: MySQL, SQLite, PostgreSQL, SnowflakeDB, MongoDB Back-end/API: Flask, FastAPI, Django, asyncio, multiprocessing Automation (bots): Telebot, Aiogram, PyWa, n8n, Zapier, Make, ElevenLabs Data scraping: Apify, requests, bs4, Selenium/ChromeDriver Version control: Git, Github, CI/CD (Github actions or gitlab)

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.

I build AI agents and automation systems that run revenue and business operations. Lead generation, outreach, pipeline management and internal workflows, working end to end so your team only steps in where judgement matters. My name is Ankit. I'm an engineer by profession with a product mindset, and I build systems, not scripts. I've spent the last few years building AI agents and automation for dozens of B2B and SaaS companies, and before that I founded a data startup that was acquired and worked in RevOps at a SaaS company. That mix matters: most automation engineers have never run a pipeline, and most RevOps people can't build. I do both. My expertise is building AI agents and automation for GTM, revenue and business operations teams, and I apply the same agent driven approach to wider business automation needs. What I build: -Lead generation and qualification systems where AI agents handle research, scoring, enrichment and follow ups -Outreach engines that write and send personalised messages across email and LinkedIn, with human approval where you want it -AI agents on top of HubSpot and Salesforce that manage pipeline, log activity and trigger next actions -Lead intelligence systems that monitor buying signals from LinkedIn, hiring activity and funding news -Customer data platforms that pull CRM, marketing and customer success tools into one account view -Operations automation: order pipelines, resource allocation, task routing and internal workflows Recent projects: -Fully automated lead gen system where separate AI agents handle sourcing, qualification, auto response and follow up, running daily without manual input. -GTM audit platform that scores a company's revenue engine across six dimensions, benchmarks against competitors and produces a sequenced automation roadmap. -Customer data platform connecting CRM, marketing and CS tools into a single pipeline view, drafting outreach from data already in those systems. -Standalone lead intelligence agent gathering buying signals from LinkedIn posts and public sources, routing qualified accounts into the sales pipeline. -Ecommerce manufacturing pipeline where AI agents move every WooCommerce order through 10 production stages, allocating work to staff based on availability. -Agentic property system for the Turkish market: agents find properties, list them across portals and manage the full workflow through a Notion dashboard. How I work: I map your workflow first, then design the system, agree where humans stay in the loop and build in short sprints. You see working output within two weeks, and everything ships with documentation so you own it. I communicate directly and often. No account managers between us. Stack: Claude Agent SDK, LangChain, LangGraph, n8n, Supabase, HubSpot, Salesforce, Claude API, OpenAI API, Apify, Clay, Instantly If your pipeline runs on manual prospecting, copy paste follow ups or spreadsheets nobody trusts, or you are spending way too much time on repetitive tasks then send me a short description of your workflow and I'll tell you honestly whether automation makes sense for it.

Healthcare AI built by an engineer who knows buyers are right to filter out generalists. Medical imaging on DICOM, clinical NLP that handles negation and hedging, HIPAA-aware PHI pipelines, patient-facing apps that respect privacy for hospitals, radiology centres, pharma R&D, CROs, medical device makers, health-tech teams. I work with the Brainy Neurals team as the healthcare-AI engineer. My focus sits between "we have clinical data" and "validated AI helping clinicians" model design, PHI handling under your BAAs, on-prem vs cloud, validation against radiologist ground truth, shipping into EHR or device workflow. WHAT I BUILD / IN HEALTHCARE Medical imaging AI — DICOM ingestion, de-identification per Safe Harbor, organ and lesion segmentation on CT and MRI, abnormality detection, NIfTI and ITK pipelines, 3D Slicer. Built on MONAI, nnU-Net, TotalSegmentator, RadImageNet, validated against radiologist consensus. Clinical NLP and document AI note summarisation, ICD and SNOMED mapping, negation and hedging, family-vs-patient history disambiguation, medication extraction, lab-report parsing, prescription OCR. The grammar of clinical text is its own thing; I treat it accordingly. Pharma and clinical research clinical protocol to eCRF extraction (demographics, vitals, inclusion-exclusion, study-arm), systematic review using RoB2, GRADE, PRISMA, citation validation, batch record digitisation, evidence assembly. Patient-facing apps GI symptom-trackers with AI food and lifestyle recommendations, diabetic and gut-condition dish-suggestion, telehealth onboarding, chronic-care intake, mental-health support. Flutter or React Native fronts, FastAPI backends. Hospital workflow AI appointment automation, triage, hand-hygiene monitoring, fall-detection in wards (pose-based, no face recognition), bed-occupancy. THE STACK / FOR MEDICAL & PHARMA Imaging: MONAI, DICOM, NIfTI, ITK, 3D Slicer, nnU-Net, TotalSegmentator, RadImageNet, modality fusion. Clinical NLP and docs: Docling for medical PDFs, DocTR and TR-OCR for prescription and lab OCR, GPT-4o multimodal for complex layouts, Claude and Gemini for clinical reasoning, regex and Pydantic validation, NegEx-style negation with LLM verification. Knowledge: Neo4j for clinical knowledge graphs (drug-drug interaction, condition-symptom, contraindications). pgvector or Qdrant for medical literature. RAG over institutional protocols, authoritative sources only. Infrastructure: FHIR and HL7 v2 with EHRs, OMOP CDM for research, FastAPI for clinical APIs, Flutter and React Native for patient apps, AWS and Azure inside client BAAs, on-prem Ollama and vLLM where data cannot leave. WHO I BUILD FOR Hospitals and clinics radiology AI, patient-flow analytics, EHR decision support, ward-safety. Radiology centres, DICOM pipelines, organ and lesion segmentation, second-read AI, reporting integration. Pharma R&D and CROs clinical protocol extraction, systematic review, eCRF generation, evidence assembly. Medical device (pre-clearance) model prototyping, validation harnesses, dataset curation. I do not claim FDA-cleared deliverables; I build the substrate that goes through your regulatory team. Telehealth and health-tech patient intake, AI triage, multilingual symptom-checker. Specialty practices dental, dermatology, gastroenterology, oncology, mental health. Specialty-tuned models on small datasets. Health insurance claim review, prior-auth triage, appeals support, evidence extraction. HOW I WORK / WITH HEALTHCARE BUYERS Discovery is a 30-minute call: data sources, clinical workflow, compliance posture (HIPAA, GDPR, local). By the end I tell you whether the use case is feasible, validation plan, where ground truth comes from, how PHI flows. If it touches a regulated device pathway, I tell you what is in scope for me and what your regulatory team owns. Pricing is fixed-scope per milestone feasibility, dataset and PHI design, model build, validation, integration handoff. Hourly only for maintenance after deploy. THE BRAINY NEURALS BACKING I work with the Brainy Neurals team 15 AI engineers, NVIDIA Inception Partner, AWS Activate, Microsoft for Startups. When a project needs ward-monitoring cameras, edge deployment on hospital hardware, RAG over clinical docs, or workflow automation around the AI, that capacity sits with the team I bring them in cleanly, you do not manage multiple vendors. For pure healthcare model and clinical-NLP work I lead end to end myself. LET'S TALK / IF You are inside healthcare or pharma, you have a clinical workflow AI can genuinely help with, you understand the validation and compliance work this requires, and you want a senior engineer who has built medical imaging, clinical NLP, and patient-facing apps before not a generalist learning HIPAA on your project. Tell me your data, workflow, compliance. I reply within 24 hours with feasibility, validation approach, milestones.