We are seeking a highly skilled Senior Software Engineer with extensive experience in Python development. The ideal candidate should possess not only technical expertise but also excellent communication skills to collaborate effectively with cross-functional teams. Your role will involve designing, developing, and maintaining robust software solutions while ensuring clarity in technical discussions. If you are passionate about coding and thrive in a dynamic environment, we want to hear from you!

I'm an independent inventor (Massachusetts LLC, patent-pending) developing a portable sports-training device that uses a projected laser line and a global-shutter mono camera to measure the angle of a small metal striking surface at the moment of impact. Target accuracy is ±0.5° on the angular measurement, with measurement latency under 2 seconds, and direct-sunlight robustness as a key engineering risk. This is an end-to-end Phase-0 feasibility engagement. The working assumption is laser-line + global-shutter mono camera with bandpass filtering, but I want your read on whether that's the right approach for this accuracy and these conditions. I have a strong lean, not a closed decision, and I'd rather you push back early than build something the wrong way. Once we align on the approach, you'll spec the bench rig (camera model, laser modules, filters, optics, baseline geometry, target mounting); I'll source the parts from your BOM and either ship the components for you to assemble or assemble and ship a built rig, your preference, whichever fits your workflow best. From there you capture data under controlled and outdoor conditions, develop the detection and calibration pipeline, and deliver a working codebase plus a written accuracy/robustness report. Hardware is returned to me on completion (or retained for a follow-on engagement if we both want to continue). What you'll deliver: 0. Approach review + rig spec. A short written deliverable (2–4 pages) covering: (a) your read on the proposed sensing approach, affirm + refine, or argue for an alternative with reasoning and a specific recommendation; (b) a bench-rig BOM with specific parts (camera model, laser modules, bandpass filters, optics, mounting, target plate) sized for the working distance and accuracy spec; (c) laser-to-camera baseline geometry with your reasoning, and recommended calibration targets. I'll source the parts from your BOM. We'll decide together whether I ship components for you to assemble or assemble and ship a built rig, whichever you'd rather. 1. Rig assembly or acceptance + baseline capture. Receive shipped parts (or built rig), assemble or validate alignment as appropriate, confirm basic optical performance against the M0 spec, then capture a baseline dataset (~200 frames per configuration) under controlled indoor lighting. Photos of the as-built rig and a setup diagram included. 2. Detection pipeline. A Python/OpenCV module that extracts the projected laser line with sub-pixel accuracy from frames at 60–100 fps. Sub-pixel line fit (Steger, Gaussian, parabolic) or weighted centroid, your choice with a short justification. 3. Calibration framework. Documented procedure and accompanying script for mapping pixel displacement to angular displacement of the target plate, accounting for camera intrinsics, lens distortion, and laser-to-camera baseline geometry. Validation against ground-truth rig angles. 4. Robustness data capture + analysis. Re-capture under (a) bright indoor with mixed daylight and (b) direct outdoor sunlight, for both laser variants with and without matched bandpass filters. Quantified accuracy + jitter per condition. 4–8 page PDF report comparing visible-red + bandpass vs. near-IR + matched bandpass. 5. Stretch (optional milestone): First cut at deriving angle-at-impact from a short pre/post-impact image sequence, pseudocode or working prototype, whichever fits the time budget. Deliverable format: Well-commented Python module(s) in a Git repo I'll provide, a README that walks a junior engineer through running the pipeline end-to-end, the captured datasets (raw frames + ground-truth angles), and a PDF report. What I'm looking for: - Comfort giving an unambiguous engineering recommendation: "use this approach with these parts" or "don't and here's why, and here's what to do instead." Phase 0 succeeds or fails based on the judgment in Milestone 0 as much as the algorithm in later milestones. - 5+ years of practical computer vision work, with shipped projects involving line/edge detection, sub-pixel feature localization, or structured-light triangulation. - Comfort doing your own benchtop work; mounting, alignment, basic optics handling. - Strong Python + OpenCV; comfort with NumPy/SciPy for the line-fit and calibration math. - Camera calibration experience (OpenCV calibrateCamera, distortion coefficients, projective geometry). - A workspace where you can run an outdoor sunlight test safely and legally with a Class-2 visible-red laser and a Class-1 IR laser module. - Bonus: prior work with laser triangulation, structured-light scanning, or sports/motion-tracking applications. - Bonus: experience deploying CV pipelines to Raspberry Pi or ESP32-S3-class hardware (potential follow-on scope). Engagement: - Fixed-price (preferred): $5,000–$7,500 total, paid across 5 milestones (approach review + rig spec → baseline capture → detection pipeline → calibration → robustness report). - Hourly alternative: $70–$140/hr with a 75-hour cap, then re-scope. - Duration: 5–7 calendar weeks (approach-review phase happens up front; ~1 week round-trip shipping after rig build). - Weekly 30-min check-ins (US Eastern preferred; flexible). - Hardware: shipped to you fully insured at my cost. Returned (insured, my prepaid label) on completion, or retained for follow-on engagement. - Possible follow-on: porting the pipeline to Raspberry Pi / ESP32-S3, IR laser variant tuning, integration support for the next prototype phase. Before we start: Short NDA + IP assignment signed before I ship the kit, share the technical design doc, or grant repo access. Upwork's standard terms transfer IP on payment, but I want a standalone signed PIIA on file as well, routine, less than 1 hour of your time. To apply, please include: 1. 1–2 examples of prior CV work involving sub-pixel localization, line fitting, or laser/structured-light triangulation. Paragraph + GitHub or paper link. 2. Three or four sentences on your approach to extracting a sub-pixel laser line centroid from a single frame. 3. Confirm you have a workspace where you can run both indoor and outdoor (direct-sunlight) image captures with a small bench rig, and that you're comfortable assembling the shipped kit. 4. Whether you prefer fixed-price or hourly, and your proposed milestone breakdown. 5. Without committing to a final answer until you've seen the full spec, a quick take: do you think projected laser line + global-shutter mono camera is the right sensing approach for ±0.5° angular accuracy at 60–100 fps under direct sunlight, or would you steer me toward a different approach? Two or three sentences. Looking forward to talking with strong candidates. Jason

I'm a retired entrepreneur and active investor looking for a skilled Claude AI practitioner to serve as a private tutor and advisor. I use Claude regularly and have a working Microsoft 365 integration in place, but I want an experienced guide to help me unlock advanced capabilities and build efficient, reusable workflows tailored to my work. This is not a beginner engagement. I learn quickly, prefer direct feedback over hand-holding, and want sessions focused on my actual use cases — not generic training. Topics to Cover - Claude Projects — structure and strategy for ongoing, organized work - Investment and general research — synthesizing company, market, and topic information efficiently - Correspondence — drafting polished emails in Outlook that match my voice with minimal editing - Document analysis — extracting key information from legal, financial, and fund documents - Microsoft 365 add-ins — what's available and genuinely useful for Word, Excel, and PowerPoint - Voice input and dictation — getting started and optimizing as a primary input method - Workflow building — creating persistent, reusable tools rather than starting from scratch each session - Agents, skills, and connected tools — connecting external tools, leveraging agentic capabilities, and building autonomous workflows - Prompt craft — advanced techniques applicable across all of the above Ideal Candidate - Hands-on experience with Claude (not just ChatGPT or general AI) - Background working with business operators, investors, or executives — not primarily developers or academics - Can demonstrate real-world applications, not just theoretical knowledge - Comfortable moving at a fast pace and adapting sessions to my priorities Format Virtual sessions via video call, 60–90 minutes each. Frequency to be determined based on fit and progress. Looking to begin with 4–6 sessions and reassess. To Apply Please include: 1. A brief description of your hands-on experience with Claude specifically 2. One or two examples of business or executive use cases you've worked on 3. Your availability and hourly rate A short introductory call before committing to paid sessions is expected.

We are seeking a senior AI developer to build and enhance AI models for our business. The role involves developing, testing, and deploying AI solutions, as well as improving existing models to increase accuracy and performance. The ideal candidate should have strong experience in AI development and be able to work independently on complex projects.

Role Overview At 1,500 employees, keeping our operations running smoothly requires flawless coordination between client needs, physical assets, and inventory. We are seeking a Technical Product Lead for AI Agents & Operations to design, build, and optimize the "air traffic control" system of our company. Your mission is to own an intelligent AI Agent that acts as the central brain for our workforce. When an employee asks, "What do I do next?", the AI Agent will dynamically evaluate live client escalations, maintenance emergencies, estimated repair times, and real-time parts availability to assign the highest-leverage task. Furthermore, you will build the automation loop so that once an employee completes a task, the AI Agent automatically updates all peripheral CRM, inventory, and ticketing systems. Key Responsibilities 🧠 Algorithmic Prioritization & Orchestration Design Multi-Variable Logic: Formulate and refine the decision-making matrix the AI uses to balance competing priorities (e.g., weighing a high-revenue client escalation against a critical equipment failure based on part lead times). Context Integration: Partner with data engineers to feed the AI Agent real-time data pipelines from: Ticketing/CRM Systems (Client escalations) IoT/EAM/CMMS Systems (Maintenance emergencies and durations) ERP/Inventory Systems (Parts availability) 🔄 Write-Back & Automation Loops System Synchronization: Architect the data write-back workflows. Ensure that when an employee reports a job "complete" via the chatbot, the AI accurately updates inventory counts, closes client tickets, and logs maintenance history without human intervention. Error Handling & Fallbacks: Establish strict guardrails and human-in-the-loop triggers for when the AI encounters data mismatches (e.g., a required part is listed as "in stock" but is physically missing). 📈 Product Ownership & Adoption Drive Operational Efficiency: Measure and optimize key metrics such as Mean Time to Resolution (MTTR), parts utilization accuracy, and employee task-transition downtime. User Experience (UX): Ensure the chatbot interface is incredibly intuitive for field/operational employees who need quick, unambiguous directives. Data & API Literacy: Strong understanding of REST APIs, webhooks, and event-driven architecture. You must know how to map data flows between disparate systems so an LLM can read and write to them reliably. Logistics/Operations Mindset: Proven experience dealing with physical constraints (inventory, lead times, physical maintenance scheduling). AI Framework Knowledge: Familiarity with AI agentic workflows (e.g., function calling, tool use, frameworks like LangChain, CrewAI, or AutoGen).

Need an AWS Rekognition Custom Labels expert to improve an image classification model for identifying plumbing parts. Current model accuracy is approximately 55%. Dataset consists of approximately 300+ images per item captured with a Foldio turntable. Need assistance with: Dataset review Training strategy Classification vs object detection recommendations Improving model accuracy to 90%+ AWS Rekognition Custom Labels implementation Experience with computer vision and AWS Rekognition required. Deliverables: Review the existing dataset Create a new image capture strategy Train the model Test the model Document the entire process 2 hours of screen-sharing sessions explaining everything

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)

Looking for an experienced Claude AI expert to help build and optimize AI workflows, agents, prompts, and automations. This is a immediate position to start right away. Needs: Strong Claude / Anthropic API experience Advanced prompt engineering AI agent and workflow automation experience API integrations Python, JavaScript, or Node.js RAG / vector database experience preferred Please share relevant Claude projects.

I am looking for a developer or agency to build a web platform called First Responder Academy. The goal is to create the most comprehensive AI-powered training platform for firefighters, EMTs, and paramedics. The platform will initially focus on: 1. Firefighter Oral Board Preparation 2. EMT NREMT Preparation 3. Paramedic NREMT Preparation 4. Firefighter I Certification Preparation 5. Firefighter II Certification Preparation Core Features Required: * User accounts and login system * Membership and subscription management * AI-powered training simulator * Progress tracking * User dashboard * Mobile-friendly design * Modern, professional interface * Ability to scale over time AI Training Features: The AI should be able to: * Conduct firefighter oral board interviews * Score responses * Provide detailed feedback * Generate EMT and paramedic scenarios * Generate Firefighter I and II questions * Create multiple-choice exams * Provide remediation and study recommendations * Increase or decrease difficulty based on performance Knowledge Base Features: The system should allow: * Uploading NREMT skill sheets * Uploading EMS protocols * Uploading Firefighter I and II standards * Uploading department-specific hiring packets * Uploading study guides and training documents The AI should reference uploaded materials when generating questions and grading answers. EMS Features: * EMT training mode * Paramedic training mode * NREMT-style testing * Practical scenario evaluations * Protocol-based learning * Trauma and medical assessment training * Airway management training * Cardiology training Cardiology Module: A major future feature will be ECG interpretation. Requirements: * ECG image library * Random ECG presentation * Student interpretation * AI grading and feedback * Rhythm recognition training * Treatment decision evaluation Firefighter Features: * Firefighter Oral Board Simulator * Firefighter I Exam Preparation * Firefighter II Exam Preparation * NFPA / IFSAC-based content * Scenario-based learning * Multiple-choice testing * Leadership and decision-making exercises Department-Specific Training: The platform should eventually allow users to: * Upload local EMS protocols * Upload department hiring packets * Upload mission statements and values * Receive customized training based on those documents Progress Tracking: The platform should track: * User scores * Weak areas * Practice history * Completion rates * Recommended study topics * Improvement over time Admin Features: * Upload and manage training content * Upload and manage protocol documents * Upload and manage ECG images * Create and edit question banks * View user analytics * Manage subscriptions Technology Preferences: * Modern scalable architecture * AI integration using OpenAI API * Secure user authentication * Cloud-hosted * Responsive design * Easy content management Design Style: * Professional * Modern * Clean * Fire and EMS themed * Dark mode preferred * Red, black, gray, and white color palette Important: I am looking to build an MVP first, not every feature immediately. Phase 1 priorities: 1. User accounts 2. AI training simulator 3. Oral board preparation 4. EMT preparation 5. Paramedic preparation 6. Firefighter I and II preparation 7. Progress tracking 8. Membership system Future phases can include ECG image testing, advanced protocol integration, department-specific customization, and additional first responder training programs. Please provide: * Estimated timeline * Estimated cost * Recommended technology stack * Examples of similar projects * Suggestions for MVP development

About Us We are a forward-thinking AI enterprise software company building governance solutions. Our systems combine Python engineering, Natural Language Processing, and Machine Learning to deliver secure governance solutions. We’re seeking a Back-End Python Engineer with expertise in AWS deployed applications, GITHUB CI/CD pipelines, DJANGO, ML Pipelines, Endpoint Integration, Sagemaker, containerization, Use of AI to design front end applications and debug code. Key Responsibilities Design, develop, and maintain back-end services in Python to support software application Debug Application for Quality and Assurance Build Data Connectors for Application Integration Implement new features with front end design as needed Containerize and deploy services across AWS infrastructure. Build and scale RESTful APIs and microservices (Django + DRF) that integrate into automated pipelines. Tune system performance (network, I/O, memory, GPU utilization) for optimization. Architect and maintain databases (SQL & NoSQL), ensuring query optimization, high availability, and caching (Redis). Integrate background processing (Celery) and real-time communication (WebSockets) into containerized environments. Collaborate with DevOps, front-end, and AI/ML teams to deliver end-to-end automated workflows. Apply best practices in system design (SOLID, DRY, KISS), Python standards (PEP8), and secure infrastructure deployment. Qualifications Core Skills Proficiency in Python (OOP, async, functional programming, data structures). Expert-level knowledge of AWS Infrastructure (deployment, operators, CI/CD, scaling). Strong background in containerization (Docker, Podman) and Kubernetes-native orchestration patterns. Experience supporting AI Dev automation workflows and integrating back-end services with automated pipelines. Deep knowledge of Django & DRF: ORM, serializers, view sets, permissions, HTTP methods. Advanced database design & optimization for high-throughput applications. Familiarity with Redis caching, Celery task queues, and uWSGI/ASGI communication layers. Solid testing skills (pytest/unittest) and CI/CD pipelines with Git. Preferred Expertise Hands-on experience with GPU-enabled workloads and hardware acceleration in containerized environments. Familiarity with infrastructure automation tools (Ansible, Terraform, or similar). Agile/Scrum team experience and use of task tracking (Jira, Trello). What We’re Looking For We want an engineer who: PRIORITIZES SECURITY OF SYSTEMS AND INFRASTRUCTURE ACROSS SECURITY FRAMEWORKS Builds automation-first systems that support AI Dev workflows from code to deployment. Thinks about performance and scalability at the infrastructure + software level. Collaborates across teams (DevOps, AI/ML, product) to deliver fully integrated, automated platforms.