US Upwork job post (1) Job Title US B2B Outbound Sales Consultant for Hardware Inspection & Supplier Audit Services (2) Short Project Overview (a) We are looking for a senior US outbound marketing consultant to help us design a practical, compliant, and effective outbound strategy for entering the US market. (b) This is a consulting project, not a cold-calling role. We need someone who understands B2B outbound for service providers targeting Supply Chain, Quality, Procurement, Operations, and Manufacturing leaders. (3) About Our Company (a) CSO Projement (csoprojement.com) is a quality management service provider. We currently support Western companies with factory inspections, supplier audits, production monitoring, sourcing support, and quality-management services in Asia. (b) Our customers are typically companies that manufacture, outsource, or buy physical products or components in Asia. We often support companies that need to reduce supplier risk, improve quality, validate factories, solve production problems, or manage supply chains in Asia. (c) We are now exploring how to expand our inspection and audit services into the US market. (4) What We Need Help With (a) We want to build a US outbound outreach strategy, primarily based on cold calls, supported by email, voicemail, LinkedIn, SMS, and WhatsApp where appropriate and compliant. (b) We need help understanding what works in the US market for our type of service and our target personas. (c) The consultant should help us answer questions such as: (i) Which US companies should we target first? (ii) Which personas are most likely to respond? (iii) What messaging will resonate with Supply Chain and Quality leaders? (iv) How should we structure cold calls and follow-up sequences? (v) When should we use email, voicemail, LinkedIn, SMS, or WhatsApp? (vi) What should we avoid because it may be ineffective, spammy, non-compliant, or damaging to our brand? (vii) What KPIs should we track during the first test campaign? (5) Required Experience (a) The ideal consultant should have strong experience with: (i) US B2B outbound marketing. (ii) Outbound strategy for service providers, not only SaaS companies. (iii) Selling or marketing to Supply Chain, Quality, Procurement, Manufacturing, Operations, or Engineering personas. (iv) Cold-call strategy, scripts, and objection handling. (v) Multi-channel outbound sequences. (vi) Messaging for need-based services. (vii) CRM workflow, lead tracking, and campaign KPIs. (viii) Practical US outreach compliance, including commercial email rules, opt-out practices, calling restrictions, SMS limitations, and consent-based communication. (ix) You do not need to provide legal advice, but you must know how to design outreach that is professional, conservative, and compliant. (6) Expectations (a) We expect the consulting sessions to produce: (i) Recommended US ICP and target persona definition. (ii) Recommended outbound strategy for CSO inspection and audit services. (iii) Cold-call strategy and suggested call flow. (iv) Voicemail, email, LinkedIn, SMS, and WhatsApp usage guidelines. (v) Suggested compliant outreach sequence. (vi) Messaging framework for Supply Chain and Quality personas. (vii) Discovery-call questions. (viii) Objection-handling framework. (ix) KPIs for testing and improving the campaign. (x) Training notes for appointment setters or callers. (7) Ideal Candidate Profile (a) You are a good fit if you: (i) Have designed US outbound campaigns for B2B service companies. (ii) Understand how senior managers and executives respond to cold outreach. (iii) Know how to reach Quality, Supply Chain, Procurement, Operations, or Manufacturing decision makers. (iv) Can give practical advice based on real campaign experience. (v) Can explain what works, what does not work, and why. (vi) Can help us avoid generic lead-generation tactics. (vii) Can create a clear first-test plan before we hire callers or appointment setters. (8) What We Do Not Want (a) We are not looking for: (i) A junior cold caller only. (ii) A generic lead-generation agency with no strategy input. (iii) Someone who only sells email blasting. (iv) Someone who recommends aggressive or non-compliant outreach. (v) Generic scripts that are not adapted to our market. (vi) A consultant without experience relevant to our circumstances. (vii) A response to this job post made entirely by an AI agent. (9) Screening Questions - Please answer "Yes/No" or in a very short sentence. (a) Please describe your experience with US B2B outbound campaigns targeting Supply Chain, Quality, Procurement, Operations, Manufacturing, or similar personas. (b) Have you worked with B2B service providers to companies selling physical products under their own brand? (c) How would you approach cold outreach for inspection, supplier audit, or quality-management services? (d) What outreach practices would you avoid in the US market?

We are looking to do a new build of our patch bay label designer tool. Customers are currently able to pick their model of patch bay, add text labels, colorize, and group. They can then checkout and it creates print ready artwork for our team to produce. The UI is a bit clunky and there are some features we want to add such as an admin tool to add and edit labels. You can see the current tool here: https://create.traceaudio.com/

### Job Description: AI Chatbot Developer We are excited to announce an opening for an experienced and innovative developer to join our dynamic team in the pursuit of creating an advanced AI Chatbot. This chatbot will be designed to perform essential business functions, including but not limited to lead generation, quoting, and providing exceptional customer support. Our ideal candidate will possess a robust background in AI technologies, particularly in the realm of chatbot development, and will be equipped with outstanding problem-solving skills that enable them to tackle complex challenges with creativity and efficiency. As a key member of our development team, you will collaborate closely with various departments to gain a comprehensive understanding of our specific operational needs and requirements. Your ability to translate these needs into a functional and user-friendly chatbot solution will be critical to enhancing our overall operational efficiency. We are looking for someone who is not just technically proficient but also possesses a keen sense of business acumen to ensure that the chatbot aligns with our strategic goals. In this role, you will be responsible for various aspects of the chatbot development lifecycle, including but not limited to: - Designing and developing the conversation flow and user interface of the chatbot, ensuring it is intuitive and engaging for users. - Implementing natural language processing (NLP) capabilities to enable the chatbot to understand and respond to user inquiries accurately. - Integrating the chatbot with existing systems and databases to facilitate seamless access to information necessary for lead generation, quoting, and customer support functions. - Conducting rigorous testing and quality assurance to ensure the chatbot performs reliably and meets user expectations. - Analyzing user interactions and feedback to continuously improve the chatbot's performance and expand its capabilities over time. - Staying current with the latest advancements in AI technologies and chatbot development to incorporate best practices and innovative solutions. You will also play a crucial role in training team members on how to utilize the chatbot effectively and will be expected to provide ongoing support and maintenance to ensure the chatbot remains up-to-date and functional. If you have a passion for artificial intelligence, a deep understanding of customer engagement strategies, and a desire to make a significant impact within our organization, we would love to hear from you! Join us in revolutionizing the way we interact with our customers and streamline our business processes through cutting-edge technology. This is a fantastic opportunity for someone looking to advance their career in a fast-paced, forward-thinking environment. Apply today and be part of our exciting journey towards enhancing our customer experience through AI!

Conversational AI / LLM Consultant We are looking for a Conversational AI and LLM specialist to support the strategy, design, development, testing, and improvement of AI-powered chatbot and voice automation solutions across multiple business groups. Responsibilities: Help identify, evaluate, and prioritize Conversational AI and LLM use cases across defined business units. Advise on best practices for Conversational AI strategy, LLM architecture, prompt design, orchestration, retrieval, integrations, and development. Recommend improvements across AWS services, Amazon Lex integrations, LLM workflows, and supporting AI infrastructure. Collaborate with the development team on chatbot, voice bot, Lex, and LLM-based implementations and configurations. Conduct QA testing to validate Conversational AI functionality, accuracy, performance, reliability, and user experience. Support the development of solution frameworks, automation workflows, dashboards, application management tools, and fulfillment processes. Assist in designing and extending multilingual Conversational AI solutions in English and Spanish. Support multiple lines of business, call flows, customer journeys, and AI-assisted workflows. Ideal Candidate: Experience with Conversational AI, LLMs, and chatbot or voice automation systems. Familiarity with Amazon Lex and AWS AI services is helpful, but broader LLM architecture experience is equally important. Strong understanding of prompt engineering, AI orchestration, integrations, QA testing, and production AI workflows. Ability to translate business requirements into practical AI-driven solutions. Experience with multilingual conversational design, especially English and Spanish, is a plus.

We need a senior architect to design and build a multi-model routing control plane, then lead a small senior team through the build. The control plane sits in front of a family of AI systems and decides, per request (text, image, video), the optimal path across cost, quality, latency, business value, and sovereignty (data residency, rights, and cultural fit): cache and reuse, a small or on-device model, an open-weight, fine-tuned, or sovereign model, or a higher-cost frontier fallback. It routes across compute too: CPU, GPU, inference accelerators, on-device, and edge. Core KPIs: the share of eligible workload kept off frontier accelerators and the resulting cost reduction on a representative workload, plus sovereignty compliance, with no quality regression. This is not a chatbot and not a wrapper over hosted APIs. You own the architecture, define the routing logic, and lead execution. You think in systems, not individual model calls. Context The router is one component of a larger AI platform. It must be model-agnostic: open-weight, fine-tuned, and proprietary models swap in and out behind a stable interface without rearchitecting. A separate team owns the models you route to. The engagement is a 60 to 90 day POC with a working router demo (text-first, with a defined path to image and video), followed by technical leadership through the build. What you'll own Control plane: intake and normalization, classification, routing taxonomy, model-selection logic, fallback hierarchy, cache and reuse rules, telemetry, and the eval feedback loop. Routing that is learned and calibrated, not just static rules: predict per-query difficulty and expected quality, and escalate on confidence thresholds. Comfort with cascades and speculative decoding is expected. Routing across cost, quality, latency, and policy. In constrained environments some requests must stay local regardless of cost. Model-agnostic interface: clean, stable contracts so models and execution paths swap without rework, and the separate model team can work independently of the routing layer. Cost optimization across compute: exact and semantic cache, prefix/KV cache reuse, output reuse, batching, small-model routing, CPU offload, and on-device/edge execution, with a clear fallback hierarchy. The goal is to move most eligible workload off frontier accelerators without degrading output. Generative caching and reuse: caching text is easy; image and video are not, since the same prompt should produce variation rather than an identical result. We need credible reuse at the asset or component level, not just for text. Eval loop: scores output quality by domain and flags weakness so the training team can target fixes instead of retraining broadly. Track quality vs intent, failure modes, cost per route, latency per route, cache hit rate, fallback rate, and regeneration rate. Execution and leadership: architecture blueprint, POC scope, milestones, infra assumptions, and risks leadership can review, plus hands-on architecture review and task breakdown. You'll lead a small senior team, and one of your first deliverables is recommending its exact composition (see screening questions). Ideal background Led or architected production AI infrastructure across several of: multi-model orchestration and LLM routing, multimodal, model serving, inference cost and GPU reduction, CPU and on-device inference, open-source and fine-tuned deployment, cascades and speculative decoding, semantic and prefix caching, eval pipelines, and AI observability. Deployed in at least one constrained environment: on-prem, self-hosted, air-gapped, or data-residency-restricted. You know what breaks when you can't lean on a single cloud. Can lead: set architecture, break down work, review the team's output, and keep the build on track. Tools matter less than the ability to architect the system correctly and lead execution. Not a fit: basic chatbot workflows, hosted APIs only, or prompt engineering alone. Deliverables Control plane blueprint, routing taxonomy, POC plan with milestones and success criteria, and an eval/feedback framework, with a working router demo as the 60 to 90 day target, then technical leadership of a small team through the build. Screening questions The most relevant AI routing, model-serving, or inference infrastructure system you personally designed or built: what was routed, which models or execution paths, and what role did you own? How would you design a router that chooses between cache/reuse, a smaller or local model, an open-weight or fine-tuned model, or a frontier fallback, across CPU and GPU? Where do learned routing, cascades, or speculative decoding fit? For generative image or video requests, how would you approach caching or reuse when the same prompt should still allow variation? Be specific. What metrics and eval loop would you use to prove the router cuts cost without degrading quality, and to help a separate training team find weaknesses? Beyond yourself, what team would you staff to hit these deliverables in 8 weeks? Give the roles, seniority, and headcount, how you'd split the work, and flag any deliverable that 8 weeks and a team of roughly 4 engineers can't realistically cover. To apply Answer the five questions, summarize your most relevant routing or inference-infrastructure work (repos, writeups, talks, or architecture you can describe), and give your high-level approach to a control plane that routes across cost, quality, and sovereignty while preserving quality. Note your availability, your rate, whether you've led a small engineering team before, and the team you'd staff to hit the deliverables in 8 weeks.

EroFlow Intelligence is an enterprise-grade, autonomous AI orchestration pipeline designed to mitigate global supply chain disruptions for aerospace manufacturing. Built using a multi-agent framework, the system automates the entire lifecycle of risk detection, impact analysis, and procurement mitigation without requiring human intervention for standard operational anomalies. The architecture coordinates three specialized, asynchronous AI agents that communicate via a centralized event bus to solve complex logistical bottlenecks in real-time. Core Agent Architecture & Workflow 1. The Sentinel Agent (Data Ingestion & Extraction) Role: Continuous Global Monitoring. Function: Utilizes advanced LLM-driven web scraping and unstructured data extraction to monitor global news feeds, geopolitical shifts, weather anomalies, and shipping port telemetry. Trigger: If it detects a disruption (e.g., a port strike or critical mineral shortage), it extracts key entities (materials affected, estimated delay times) and passes a structured JSON payload to the orchestration layer. 2. The Impact Assessment Agent (Predictive Modeling) Role: Deep Cross-Referencing & Analytics. Function: Upon receiving a trigger, this agent cross-references the disrupted material with the company’s internal ERP (Enterprise Resource Planning) database and current inventory levels. Output: It runs a predictive analysis to determine exactly which production lines will stall and calculates the financial risk, assigning a high/medium/low priority score to the event. 3. The Mitigation & Logistics Agent (Autonomous Execution) Role: Operational Resolution. Function: If the risk score exceeds a specific threshold, this agent is authorized to take action. It autonomously queries pre-vetted alternative suppliers via APIs, negotiates standard volume pricing based on historical contract data, drafts a comprehensive procurement proposal, and queues the purchase order for final human sign-off (or executes it automatically if under a certain dollar cap). Technical Stack (The Blueprint) Frameworks: LangGraph / CrewAI (for multi-agent state management and deterministic routing). Core Language: Python 3.11+ Data Layer: PostgreSQL (for ERP syncing) & Pinecone / Qdrant (Vector database for storing and querying supplier contract PDFs and historical compliance documentation). LLM Orchestration: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet utilized via structured outputs (Pydantic parsing) to ensure strict API data integrity. Hosting & DevOps: Containerized via Docker, orchestrated via Kubernetes, and deployed on AWS with asynchronous task queues managed by Celery and Redis. Quantifiable Business Results (The Hook) 92% Reduction in supply chain anomaly response time (from 48 hours down to 14 minutes). Automated Recovery: Successfully mitigated over 140 potential production line stalls autonomously in simulated stress tests. Cost Efficiency: Saved an estimated $1.2M in expedited shipping fees by predicting bottlenecks 10 days before they impacted manufacturing floors.

I am a partner at a recruiting firm seeking a conversational AI tool to enhance our communication with candidates and clients. Our business is highly conversational, and we primarily use LinkedIn and Loxo. We need a tool that can efficiently manage and personalize our interactions, potentially integrating with our existing platforms.

I’m looking for a senior AI app developer who can help me build an AI-powered MVP while also guiding me through the technical decisions. This is not just a coding task. I want someone who can think through the product, recommend the right architecture, explain tradeoffs, and build the first working version. The ideal person should be comfortable with OpenAI/LLM integrations, full-stack development, database design, authentication, deployment, and startup-style MVP execution. I’d like to work with someone who can act almost like a technical partner: build the product, teach me what is being done, and help me understand how to maintain or scale it later.

I am building Dewy, an early-stage construction technology platform focused on construction buyout and subcontractor quote intelligence. The first MVP is intentionally narrow: users should be able to upload subcontractor quote/proposal documents and receive structured outputs showing included scope, exclusions, assumptions, qualifications, cost structure, alternates, allowances, and potential risk flags. I have already developed the product concept, construction logic, early workflows, and prototype direction using Codex/AI tools. I am not looking for someone to invent the product from scratch. I need a senior AI product engineer who can review what I have, determine what is usable, define a clean MVP architecture, and help turn the current direction into a working private beta. Initial scope: * Review the current prototype/code/product materials. * Identify what should be reused vs. rebuilt. * Recommend the MVP architecture and tech stack. * Define the AI document-processing workflow. * Design the structure for file upload, extraction, editable results, and export. * Help create a realistic build roadmap, timeline, and budget. * Potentially continue into hands-on MVP development if there is a strong fit. Ideal experience: * Full-stack SaaS / MVP development * AI / LLM application development * OpenAI API or similar model integrations * Document extraction or document intelligence workflows * PDF/DOCX parsing and structured data extraction * React / Next.js * Python * APIs and backend workflows * Supabase/Postgres or similar database experience * Vercel or similar deployment experience * Ability to work with a non-technical founder and translate business goals into a practical build plan This is not a full enterprise platform build yet. The first MVP should stay focused on one core workflow: Subcontractor quote documents in → structured buyout intelligence out. Please respond with: 1. Relevant AI/document extraction projects you have built. 2. How you would approach the MVP architecture. 3. Whether you recommend starting with an audit/roadmap before build. 4. Your hourly rate and availability. 5. Whether you are interested in ongoing build involvement after the initial review.

Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting