Hi We are looking for someone who is expert in Replit/Lovable to for 3 week series of workshops of app building. We are looking for someone who can enjoy imparting knowledge to kids worldwide and align with our vision of giving a level playing field to all kids around the globe. We believe most kids are being shelled by their ecosystem, and we have to expose them to the world. We are looking for someone who can enjoy the process, enjoy working with young brains and can make the workshops fun learning. If this is something you can enjoy. Let's talk. Best Sam

We are seeking an AI expert to set up segregated isolated AI system environments for business lines of inquiry and workflow. The ideal candidate will have experience with Ubuntu, Proxmox, and Multi V. Responsibilities include designing and implementing secure AI environments, ensuring system integrity, and optimizing performance. The role requires strong problem-solving skills and the ability to work independently.

I am looking for a freelancer to help me set up a personal AI-powered workstation and workflow system. I work in banking, credit underwriting, trade finance, and real estate. I use a Windows/PC environment and need a practical setup that helps me organize documents, prompts, templates, workflows, and repeatable tasks using ChatGPT, Claude, Microsoft Office, OneDrive/SharePoint, and Windows tools. The goal is not theory. I need a working system that helps me draft emails, credit memos, financial analysis prompts, presentation prompts, document review workflows, and repeatable instructions. Deliverables: 1. Recommended workstation/software setup 2. Folder and file organization structure 3. Prompt library by workflow category 4. Repeatable SOPs for common tasks 5. Basic training on how to use the system This should be fixed-price and milestone-based. Please include examples of similar workflow systems you have built.

We're a B2B AI startup selling a producer relationship management platform to enterprise insurance and investment companies. We're looking for someone to pitch alongside the founder in meetings with senior executives at carriers and distributors — and to present quantitative research reports that frame the market opportunity for prospective clients. You need proven experience pitching technical solutions and cost-benefit analysis to executive audiences, the confidence to hold your own in a C-suite room, and enough fluency in AI and data to engage substantively when the conversation goes deep. You should be comfortable with AI concepts, confident interpreting quantitative analysis, and able to speak credibly about LLM-based workflows and analytics-driven platforms without getting lost. The rest is about presence: a natural, personable style, native English fluency, and the instinct to read a room, handle tough objections, and move a deal forward. All presentation materials will be provided. The core ask is the ability to absorb them quickly and present with confidence — and it gets easier fast, since the pitch content stays consistent across engagements.

Seeking an experienced freelancer to scope an AI project focused on generating building floor plans. The ideal candidate will have a strong background in architectural design and AI technologies, with the ability to assess project requirements and provide detailed proposals.

Join our team as a senior AI Architect working closely with our product and engineer teams to design practical AI capabilities within our SaaS platform. This is a hands-on role focused on building reliable, production-grade conversational and AI-assisted features — not experimental research projects. You will work closely with product and engineering teams to design scalable AI patterns, integrate modern LLM technologies, and help shape how AI capabilities are embedded into real operational workflows. You will focus deeply on architecture, implementation quality, reliability, usability, scalability, observability, and operational robustness. This role is ideal for someone who understands both modern AI tooling and the realities of shipping enterprise SaaS software in production environments. We value people who can think critically about architecture, tradeoffs, operational realities, and long-term maintainability — not just prototype AI demos.

DESCRIPTION We're a small applied AI lab running a live, production-track AI product for an institutional financial services client. The work is technical, fast-moving, and high-stakes. We need to fill a critical infrastructure role with someone senior, collaborative, and genuinely excited about building in the current AI tooling ecosystem. THE ROLE You'll own the data infrastructure layer for an AI-powered intelligence platform built on the Microsoft Azure ecosystem. This is a hands-on engineering position — you're responsible for designing, building, and maintaining the pipelines that feed a live AI scoring engine. The environment is agentic. Data moves from 15+ heterogeneous external sources (APIs, PDFs, regulatory filings, web) through Bronze, Silver, and Gold layers into a scoring and inference system. The hard problems are extraction quality, schema normalization, pipeline reliability, and getting the right data to the scoring engine in the right shape. You'll work directly with the technical lead and engagement lead. No layers. Fast decisions. WHAT YOU'LL OWN + Data pipeline architecture and delivery across Bronze (raw ingestion), Silver (normalization, NLP extraction, entity resolution), and Gold (unified output, scoring-ready) layers + Microsoft Fabric lakehouse implementation — OneLake, Data Pipelines, Dataflows Gen2, Warehouse, and downstream system integration + Microsoft Foundry (formerly Azure AI Studio) — agent orchestration, prompt pipelines, and AI model integration within a secure Azure tenancy + Azure Data Factory orchestration for structured source ingestion +Salesforce integration via Snowflake native connector — field mapping, custom object schemas, sync reliability Extraction pipelines for unstructured sources (PDFs, regulatory filings, web content), coordinating with Azure OpenAI-based extraction agents +Data governance and security posture — all data stays within the client's Azure tenancy; data residency is non-negotiable REQUIRED: Technical Skills + Microsoft Fabric — production experience, not sandbox. You should be able to speak to Lakehouse vs. Warehouse tradeoffs, OneLake architecture, and real pipeline implementation. Microsoft Foundry / Azure AI Studio — hands-on with agent deployments, prompt flow, model endpoints, and Azure OpenAI integration within an enterprise Azure tenancy + Azure Data Factory — pipeline authoring, trigger management, connector configuration, monitoring +Snowflake — Gold layer data warehousing, schema design, query optimization, native connector usage (specifically Salesforce) + Python — data engineering contexts: pandas, PySpark, API clients, extraction scripts + SQL — complex joins, window functions, schema design; SQL Server preferred + Azure Blob Storage / ADLS Gen2 — Parquet/Delta format, access control, lifecycle management REQUIRED: AI-Augmented Development This is a hard requirement. You should be actively using AI coding tools to multiply your output — fluency with Claude Code, Cursor, and OpenAI Codex as part of your daily development workflow. If these aren't already in your stack, this isn't the right fit. We hire for multiplied output, not raw hours. REQUIRED: Demonstrable Work We don't evaluate resumes alone. Bring something — a GitHub repo, a deployed pipeline, an architecture document you authored, a case study with real numbers. We should be able to look at your work and understand what you built, what decisions you made, and why. Work under NDA is fine if you can describe it in enough detail to convey complexity and ownership. ATTITUDE & WORK STYLE Comfortable with Agile Scrum and its accompanying ceremonies. You raise issues early and help solve them. You communicate tradeoffs clearly without over-explaining. You're comfortable with evolving specs and don't need to win the architecture argument — just build the right thing within the approved stack. We're a small, senior team with low friction and direct communication. That's the environment; it works if you work with it. THE STACK The client environment has specific technology approvals. Production work runs on Azure OpenAI (client-hosted), Microsoft Fabric, Microsoft Foundry, Snowflake, Azure Data Factory, ADLS Gen2, Salesforce via Snowflake native connector, and SQL Server. LangChain, DeepSeek, and the external Claude API are not approved for this environment. NICE TO HAVES Experience with financial services or institutional investment data (SEC EDGAR, public pension filings, regulatory documents), familiarity with InvestorFlow or Salesforce Financial Services Cloud, unstructured document extraction at scale, or Azure Purview.

Project Brief: Horse Racing Handicapping Automation – MVP Project Title: Build an MVP Python script to automate horse racing handicapping using Thoro-Graph, Brisnet, and EquinEdge data. Overview / Goal I have developed a working handicapping model in Python (handicap_race_v39) that ranks horses for a race using normalized metrics from EquinEdge (Win%, GSR), Beyer figures, projected Thoro-Graph figures, and bonus signals (Thoro-Graph patterns, trip quality, hidden/sneaky good performances). The goal of this project is to create a Minimum Viable Product (MVP) that automates the end-to-end process: Input: A full race card’s documents (Thoro-Graph PDF + Brisnet PDF + EquinEdge screenshots) Output: Ranked horse selections per race with transparent bonus explanations. This is the cheapest realistic path — I want a functional working script first, not a polished GUI or production-grade system. Current State (What Already Exists) • A verified core handicapping function (handicap_race_v39) that normalizes inputs and produces ranked selections with estimated win probabilities. • Proximity-based bonus logic that assigns Thoro-Graph patterns and trip comments to the correct horse. • Bonus configuration values (e.g., Top-Pair-Top = +18, troubled but strong trip = +6 to +10, hidden trip = +12, X/bounce = -8). • Human-readable bonus summary logic. I will provide all existing code to the developer. MVP Scope (Cheapest Realistic Path) Build a single, reliable Python script that can: 1. Accept a race card’s documents (one Thoro-Graph multi-page PDF, one Brisnet PDF, and multiple EquinEdge screenshot images). 2. Extract key structured data: • Horse names • Today’s projected Thoro-Graph figure (or relevant pattern) • Beyer figures • EquinEdge Win% and GSR • Thoro-Graph patterns (Top-Pair-Top, Pair-Pair-Pair, X, bounce, etc.) • Trip quality / hidden trip signals 3. Run the existing handicap_race_v39 model with proximity-based bonuses. 4. Output clean ranked selections per race, including: • Rank • Horse name • Composite score • Estimated win probability • Key bonus explanations (why the horse received positive or negative bonuses) Key Deliverables • One working Python script (or small set of scripts) that processes a full race card. • Clear output in CSV and/or readable text format. • Basic documentation on how to run the script. • The script should handle the most common cases cleanly (even if it needs occasional manual help on very difficult pages). Technical Preferences • Python 3 (pandas, numpy, etc.) • Use of AWS Textract is acceptable for PDF parsing (I can provide AWS access or the developer can suggest alternatives). • The existing code I provide should be used as the foundation for the scoring engine. • Keep it simple and maintainable — this is an MVP. Out of Scope (to keep cost down) • Web interface or GUI • Fully automated daily processing / scheduling • Perfect accuracy on every single page (some manual review or overrides are acceptable in MVP) • Back-testing framework • Advanced machine learning models Success Criteria • The script can process a complete race card (Thoro-Graph + Brisnet + EquinEdge) and produce ranked selections for all races. • Bonus logic is applied at the horse level (not race level). • Output is clear enough that I can understand why each horse received its ranking and bonuses. • The script runs reliably on new race cards with reasonable accuracy. Timeline & Budget Guidance (Cheapest Realistic Path) • I am looking for the most cost-effective realistic solution, not the most polished version. • Realistic budget range for this MVP: $3,500 (depending on experience and location). • Timeline: 3–5 weeks is acceptable. How to Apply Please include the following in your proposal: 1. Your relevant experience with PDF parsing, OCR, or document automation (especially dense tables or racing/sports data). 2. A short description of how you would approach the Thoro-Graph PDF parsing challenge. 3. Your proposed timeline and total cost for the MVP as described. 4. Any questions you have about the existing code or scope.

We are looking for an experienced Lead Crypto/AI Engineer who can also effectively manage team operations and client accounts. Your responsibilities will include: Leading the development and optimization of blockchain solutions and AI systems. Overseeing technical project delivery, ensuring alignment with client expectations. Managing client account processes, including setup, communications, and deliverables. Coordinating and supervising workflows among global team members. Requirements: Proven experience in blockchain technology, cryptocurrency projects, and AI development. Strong leadership, project management, and account management skills. Proficiency in programming languages such as Python, Solidity, Solana, JavaScript, or equivalent. Excellent communication skills and a track record of effectively managing client relationships.

Eligibility: This role is open to U.S. citizens only due to client security and compliance requirements. Please apply through this posting only — do not contact Data-Sleek directly regarding this position. Applications received outside this channel will not be considered and reported to Upwork. Data-Sleek is looking for a Senior AI Solutions Engineer to lead our on-premise and government-cloud AI deployments. You will design, build, and deploy AI-powered data pipelines for clients who cannot use commercial cloud due to ITAR, CMMC, or other data residency constraints, beginning with a client in the aerospace and defense sector. Beyond this first engagement, you will become Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients, building the practice rather than just executing a single project. About Data-Sleek Founded in 2020, Data‑Sleek® is a U.S.-based AI and data consulting firm that helps mid-market companies build the data foundation that AI actually runs on. We own the full path — data strategy, architecture, integration, warehousing, and AI implementation — so organizations can adopt AI with confidence, stay compliant, and scale, without first hiring an internal data team. Our distributed U.S. team (San Francisco, Los Angeles, Irvine, Dallas, Chicago, and New York) partners with clients across healthcare, finance, insurance, logistics, and technology, modernizing data platforms with best-in-class tools like Snowflake, dbt, Fivetran, Tableau, and AWS. Trusted by Fortune 500 institutions and growing companies alike, Data‑Sleek turns complex data into measurable outcomes — faster insight, lower cost, and AI projects that deliver. About the Role You will own the technical delivery of AI-powered data pipelines in restricted environments where commercial cloud is not an option. The immediate engagement centers on a Product Lifecycle Management (PLM) data migration: building a pipeline that connects to a client's SharePoint on a restricted Microsoft 365 government tenant, reads engineering documents, classifies and summarizes them, detects duplicates, and rates naming-convention compliance to produce a migration-readiness report. You will start on-premise, then help the client evaluate and move to government cloud for production. Key Responsibilities AI Pipeline Development Build AI pipelines that connect to a client's SharePoint on a government cloud tenant, read engineering documents, classify them by type, generate summaries, detect duplicates, and rate naming-convention compliance in support of PLM data migration. Catalog large document repositories and produce migration-readiness reports and Excel catalogs that give clients a clear, measurable picture of their data. Engineer document-parsing workflows across DOCX, PDF, and XLSX formats, including embedding generation and database operations. On-Premise & Government Cloud Deployment Deploy on-premise first — a Mac Mini running Gemma via Ollama — standing up, serving, and tuning local inference infrastructure. Evaluate and migrate to production on Azure OpenAI (Azure Government) or AWS Bedrock (GovCloud) when the client is ready to scale. Keep deployments compliant within ITAR-sensitive, restricted-network boundaries throughout. Architecture & Cost Advisory Produce cost models and architecture recommendations that help client IT teams make informed platform decisions based on measured data, not vendor pitches. Compare deployment options — local, Azure Government, and AWS GovCloud — on cost, performance, and compliance, and explain the trade-offs clearly. Practice Building & Delivery Serve as Data-Sleek's go-to engineer for AI deployments across defense and aerospace clients. Build a reusable capability — a repeatable AI-solutions practice — rather than executing a single one-off project. What You Bring Required U.S. Citizen: U.S. citizenship is required and non-negotiable due to ITAR and client security and compliance requirements. Production LLM deployment: You have stood up inference infrastructure — not just called an API. You've handled model loading, memory constraints, failure modes, and throughput tuning in a real deployment. Local inference: Ollama, vLLM, llama.cpp, LM Studio, or TGI. You've served open-source models (Gemma, Llama, Mistral) on local hardware. Cloud AI platforms: Azure OpenAI or AWS Bedrock — at least one. Service configuration, model access, authentication, and token-based pricing. Python: Pipeline engineering — document parsing (DOCX, PDF, XLSX), API integrations, embedding generation, and database operations (SQLite, Postgres). Experience: 5+ years post-degree in software engineering, data engineering, or ML engineering. Strong Preferences Microsoft ecosystem: Entra ID, Microsoft Graph API, and SharePoint REST API at the API level. GCC High experience is a bonus. MCP (Model Context Protocol): Experience building or consuming MCP servers — a significant plus for a fast-evolving protocol. Workflow orchestration: n8n, Temporal, Airflow, or similar. The pipeline is orchestrated, not scripted. Government cloud awareness: Understanding of what FedRAMP High, IL4/IL5, and ITAR mean for cloud architecture decisions. Embeddings & vector similarity: sentence-transformers, pgvector, Qdrant, or FAISS for duplicate detection. 
Bonus (valued if present) Aerospace or defense experience: Familiarity with ECOs, BOMs, and AS9100 saves ramp time. Apple Silicon optimization: MLX, Metal acceleration, and Ollama tuning on M-series chips. Agentic frameworks: Bedrock AgentCore or Azure AI Foundry — the future direction involves agentic AI workflows on government cloud. What This Role Is Not Model training or fine-tuning. This is deployment engineering, not research. Data science or statistical modeling. The AI here is document understanding and classification, not predictive analytics. Frontend development. The deliverable is an Excel catalog and a report, not a web app. Sales or client acquisition. Data-Sleek's leadership manages the client relationship; you focus on delivery. Engagement & Compensation Remote, US-based. Occasional on-site travel to client facilities for hardware deployment and workshops may be needed. An average of 2–3 trips for the first engagement may be possible. Compensation. $40-$55/hour Why Join Data-Sleek? At Data-Sleek, you'll lead AI deployments in environments most engineers never touch — government cloud and on-premise systems where commercial tools simply aren't an option. Your work will directly shape how defense and aerospace clients adopt AI, and you'll build a reusable capability the company grows around. We focus on doing the right thing architecturally rather than selling the most expensive option, and we give our engineers the autonomy to deliver real solutions for real constraints. How to Apply If you've shipped real LLM deployments with real constraints, we'd like to hear from you. Please submit: Your resume A brief note describing one LLM deployment you've shipped — what model, what infrastructure, what data source, and what went wrong. Data-Sleek® is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all contractors.