Senior Software Engineer (AI-Focused, Contract – US) Position Summary W Energy is seeking a Senior Software Engineer (Contract) to help drive the integration of AI capabilities into our core platform. This role is focused on building AI-powered product features, not just experimenting with models—embedding intelligence directly into workflows across our upstream and midstream solutions. You’ll design and implement AI-driven functionality that improves automation and user experience. This includes leveraging LLMs, machine learning models, and modern AI tooling within a production SaaS environment. This is a hands-on role for someone who can move quickly, make pragmatic decisions, and bring AI concepts into real, scalable product features. Responsibilities • Design and implement AI-powered features within the platform (e.g., automation, recommendations, copilots) • Integrate LLMs and/or ML models into existing services and workflows • Evaluate, select, and optimize AI tools, APIs, and frameworks for production use • Collaborate with Product to translate business problems into AI-driven solutions • Build and maintain scalable backend services to support AI functionality • Profile, test, and optimize performance of AI-integrated systems • Ensure reliability, security, and cost-efficiency of AI components in production • Contribute to architecture decisions around AI integration and system design • Partner with engineering teams to embed AI into existing applications without degrading stability Requirements • 5+ years of experience as a software engineer in a SaaS or cloud-based environment • Strong backend engineering experience (RoR and/or Golang preferred) • Experience integrating APIs and working within distributed systems • Hands-on experience with AI/ML tools (e.g., OpenAI, Anthropic, Hugging Face, or similar) • Experience building or integrating AI-powered features into applications (not just experimentation) • Strong understanding of data flow, system design, and performance optimization • Experience with relational databases (SQL Server or similar) • Familiarity with microservices architecture, Kubernetes, and CI/CD pipelines • Experience deploying applications in Azure or similar cloud environments • Strong problem-solving skills with ability to work in ambiguous, fast-moving environments • Builder mindset—someone who can take an idea and turn it into a working feature quickly • Pragmatic approach to AI (focus on value, not hype) • Ability to work independently in a contract environment while collaborating closely with internal teams • Strong communication skills and ability to explain AI concepts to non-technical stakeholders Preferred • Experience with prompt engineering, embeddings, or retrieval-augmented generation (RAG) • Exposure to model evaluation, fine-tuning, or AI performance monitoring • Experience with event-driven architectures or real-time data processing • Background in energy, fintech, or other complex data-driven industries

Type: Hourly, ongoing (part-time to full-time, room to grow) Stack you'll work in: Notion, Slack, HubSpot, Google Workspace/Gmail, Claude + other LLM APIs, Zapier/Make/n8n About us We're a fast-moving sports and fan-engagement startup. We're small, we ship quickly, and we want AI woven into how the whole company operates, not as a side experiment, but as the default way we work. You'd be the person who makes that real. What you'll do Map our current workflows across sales, marketing, ops, and content, then find the highest-leverage places to automate. Build automations and agent workflows that connect our tools (Notion, Slack, HubSpot, Gmail/Google Workspace) using platforms like Zapier, Make, or n8n plus LLM APIs. Design and ship AI agents for real jobs: lead routing and CRM enrichment, content drafting, customer/fan response triage, internal knowledge search, reporting digests. Stand up the connective tissue (prompts, integrations, guardrails, and monitoring) so automations are reliable, not brittle demos. Train and enable our team: build SOPs, run working sessions, and create lightweight docs so non-technical people actually adopt what you build. Help set our AI strategy and roadmap as we scale. You're a strong fit if you Have shipped real automations and AI agent workflows in production (not just prototypes). Are fluent with Zapier / Make / n8n and at least one major LLM API (Anthropic/Claude, OpenAI). Know your way around HubSpot, Notion, Slack, and Google Workspace integrations and APIs. Can write clean prompts and think in systems: edge cases, error handling, human-in-the-loop checkpoints. Can explain technical work to non-technical people and get them to adopt it. Communicate proactively and move fast without breaking trust on things that touch customers or revenue. Nice to have Experience taking a small company "AI-native" end to end. Background in sports and/or blockchain. Comfort with light scripting (Python/JS) when no-code hits its limits. How to apply In your proposal, please: Describe one AI agent or automation you built, the tools involved, and the measurable result. Tell us how you'd approach training a non-technical team to actually use what you build. This part matters as much as the build. Share your hourly rate and weekly availability. Proposals that skip these will be passed over. We're looking to start with a small paid task and grow the engagement from there.

I am building an authentication library and a billing platform in Golang + Postgres. Auth-library is embedded only, while billing can be an embedded-library, and also a stand-alone application. The platform is intended to make it easy for Go-devs to write authentication (users register, OIDC, recover password, etc.), and manage billing their users (various billing providers in addition to Stripe, including crypto). These codebases are mostly written by AI (Claude Code and Codex) but the issue is that these tools suck. The write a lot of slop, and their systems don't work, and I don't trust them. With auth + billing, these are highly sensitive areas where we cannot afford to fuck up. Deliverables are: 1. a list of tasks the LLMs must do to refactor / fix these systems 2. a complete audit of all APIs (the surface areas / shapes that are used as system boundaries) 3. integration tests that will pass, if the LLM's code is correct 4. a complete security audit, to address any vulnerabilities / exploits possible I am only looking to hire someone in California / colorado. I want to work synchronously with them; for every hour you bill we'll be on a call together so that we can maximize communication bandwidth. I live in Denver, but I plan to move to SF soon. I'd be down to work together in person in the future, but for now we'll only be working synchronously-remotely. Human-written applications only. Template or LLM-written responses here will be rejected immediately. Please list your qualifications concisely. Don't say 'I am a Golang expert' say 'I worked at company X, and built system Y in Go which does Z.' Thanks for your time.