Job Description We are building a community where global talent is geographically unleashed, and diversity is celebrated. We champion the freedom of living and working across borders, shaping a nation without a country. WorkNomads is on the hunt for an AI Engineer to join our team, working remotely from anywhere in the world. Who We Are: - Remote engineering services, global impact: Expert engineering, design & business talent, and cross-disciplinary project teams. - Diversity celebrated: 40+ nationalities, 4 continents, one team. - Continuous growth: We invest in you, projects, coliving, and community, stretched beyond what you thought possible. Your mission at WorkNomads: - Build and deploy Agentic AI systems - Develop scalable back-end architectures using Python - Design, implement and maintain AI agents from experimentation to production Work on internal AI initiatives - Deploy AI services across cloud platforms (AWS, GCP, Azure) - Collaborate closely with colleagues to ship production-ready systems - Ensure clean integration of AI components with broader engineering infrastructure - Apply MLOps best practices to monitor, optimize, and scale AI solutions We want to hear from you if you have: - 4+ years of experience as an AI Engineer - Strong back-end development background (Python is a must) - Hands-on experience with Agentic AI frameworks and architectures - Experience with MLOps and deploying AI systems to production - Solid software engineering foundations (not purely Data Science-focused) - Bonus: DevOps and/or Data Analytics experience And beyond the technical side: - A passion for continuous learning & collaboration in a global setting - A team player mentality and belief that no one should be left behind regardless of background - A builder’s mindset: you think in systems, not just models - Comfortable working in fast-moving, experimental environments Perks of being one of us: - Flexible working hours - Competitive remuneration and social benefits - Remote work + optional coliving options for deep community connections - Growth opportunities; learn from experts, expand your craft, work across industries - Be part of something beyond work: culture, values, impact How to Apply / What Happens Next: - Send us your CV by hitting the “Apply” button. - You will hear from one of our Spotters within 5 business days with the next steps. - Meet us on MS Teams and tell us what excites you. - The next stage could be a test or a task; we want to see what you DO (not just what you say). - Along this journey, prepare to meet either our Chief Talent Gaucho, your future leader, or our Head Honcho 😉 Interest piqued? Join us! WorkNomads - A Nation Without a Country We celebrate the right of every human to be themselves and provide opportunities for global talent to shine. Diversity, social equity, and inclusion are at the core of who we are. We create an environment where different voices are respected, heard, and empowered in equal measure. We’re breaking down boundaries by designing a new nomadic economy, enabling people to do what they love and be where they want.

Role Summary We are looking for a Senior FullStack Engineer, AI to own and evolve Delta Exchange's suite of AI-powered products. You will work across multiple production AI applications: conversational support agents, RAG-powered search, code-generation copilots, and trading-strategy assistants. This is a hands-on role where you architect solutions around our existing stack, build retrieval pipelines, improve frontends with product sense, optimise inference costs, and run evaluations, collaborating closely with the product team. Key Responsibilities - Design, build, and maintain production AI applications end-to-end: backend, frontend, and inference services. - Architect RAG systems using vector databases, embedding models, and chunking strategies optimised for accuracy and latency. - Build agentic workflows with tool/function calling, multi-step reasoning, and structured output parsing, with accuracy and control as priority. - Write and iterate on system prompts, few-shot examples, and prompt chains to maximise output quality. - Implement function calling, tool-use patterns, and structured JSON/XML output handling using frontier and lightweight models from providers like Anthropic and OpenAI. - Drive cost optimisation: model selection, caching, token budgeting, and request batching at scale. - Build and maintain evaluation frameworks to measure accuracy, relevance, hallucination rates, and regression across prompt and model changes. Experience with observability tools (Sentry, Opik, etc.) is a must. - Work with message queues (RabbitMQ), caching layers (Redis), and relational databases (PostgreSQL) powering AI service backends. - Deploy and manage AI services on Kubernetes with CI/CD pipelines on AWS/GCP. - Integrate AI capabilities with third-party platforms (Telegram bots, chat widgets, etc.). - Contribute to architectural decisions: model selection, hosting (cloud APIs vs. self-hosted), and build-vs-buy trade-offs. Required Skills & Experience - 5+ years shipping production software systems. - 2 years building AI/LLM-powered applications end-to-end with real users and volume. Not prototypes. - Strong experience with RAG architectures: vector databases, embedding models, chunking/indexing strategies, and retrieval evaluation. - Deep understanding of LLM capabilities and limitations: prompt engineering, function/tool calling, structured outputs, context window management, and multi-turn conversations. - Experience with LLM provider APIs and abstraction layers (OpenAI, Anthropic, LiteLLM, OpenRouter, or similar). - Proficiency in Python (Flask/FastAPI) and/or Node.js/TypeScript (Next.js, Vercel AI SDK). Golang experience is a plus. - Hands-on experience building evals, tracking quality metrics, and debugging non-deterministic outputs in production. - Familiarity with cost optimisation: model routing, caching, token usage monitoring, and prompt compression. - Solid fundamentals in data structures, algorithms, and system design. - Experience with containerised deployments (Docker, Kubernetes) and cloud platforms (AWS/GCP). Practical understanding of k8s concepts and trade-offs is a must. Nice to Have - Experience with agentic frameworks (Vercel AI SDK, LangChain, Mastra, etc.). Even better if you have built Gen AI apps using raw HTTP calls to provider APIs and designed efficient conversation persistence to a database. - Background in fine-tuning or training open-source models. Huge plus if you can demonstrate matching proprietary model quality (Sonnet 4.6, Haiku 4.5, etc.) with fine-tuned alternatives. - Knowledge of cryptocurrency, derivatives trading, or financial systems. Helps understand how AI can improve product UX. - Open-source contributions or personal projects with real traction. Even a tool you built to solve your own problem counts. - Your day is spent mostly in AI coding harnesses (Claude Code, Codex, Droid, etc.), MCP servers, custom skills, and similar tooling. We use AI to build AI, and candidates already living this workflow will ramp up fast. - Proven ability to debug production AI systems: diagnosing tool call failures, optimising function calling patterns, refining prompts and tool descriptions under real traffic. - Extreme ownership and bias towards action. You treat production AI systems as your own, proactively improving quality, latency, and cost without waiting to be asked. You deliver your best work especially when no one is watching.

Role Description What You'll Do: - Conduct code reviews across the platform codebase. You will review pull requests thoroughly, provide clear and constructive feedback, and help maintain code quality standards. - Build and maintain integrations with enterprise systems including SharePoint (via Microsoft Graph API), GitLab, EPDM, and internal Hub services. - Develop and maintain LLM API integrations across multiple providers. - Build and maintain RAG pipelines: document ingestion, parsing, chunking, embedding generation, vector database storage, and retrieval. - Develop backend APIs in Python, including asynchronous request handling and background job processing. - Contribute to agent framework development. - Implement and maintain authentication and authorization flows. - Troubleshoot and fix production issues. - Collaborate with team on deployment, monitoring, and infrastructure needs. - Mentor junior engineers through code review, pairing, and technical guidance. Qualifications - Requires a Bachelor’s degree in Software Engineering, or a related Science, Engineering, Technology or Mathematics field. - 5+ years of job-related experience, or a Master's degree plus 3 years of job-related experience. - Agile experience preferred. - Due to the nature of work performed within our facilities, U.S. citizenship is required. Requirements - Expert Python skills — you need to be an expert writing, reading, and reviewing Python. - Experience building and consuming REST APIs. - Working experience with at least one major LLM API — token management, streaming responses, prompt construction, error handling. - Understanding of RAG patterns — embeddings, vector databases, document chunking and retrieval. - Experience with async programming or task queue systems. - Proficiency with Git and Docker usage. - Demonstrated ability to conduct thorough, constructive code reviews. Benefits - An exciting career path with opportunities for continuous learning and development. - Flexible schedules with every other Friday off work, if desired (9/80 schedule). - Competitive benefits, including 401k matching, flex time off, paid parental leave, healthcare benefits, health & wellness programs, employee resource and social groups, and more. Company Description General Dynamics Mission Systems (GDMS) engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team!

Texas Sports Academy is on the lookout for a Senior AI Engineer specializing in LLM (Large Language Model) Systems and RAG (Retrieval-Augmented Generation) Optimization. As we continue to push the boundaries of sports technology, your role will be pivotal in developing and optimizing AI-driven solutions that enhance our offerings for athletes and coaches. You will be responsible for designing, developing, and fine-tuning LLM systems that can offer personalized insights and performance recommendations based on data-driven analysis. Your expertise in retrieval-augmented generation will enable the integration of comprehensive data sources, empowering our systems to deliver high-quality, context-aware content and responses. You will work collaboratively with data scientists, software engineers, and domain experts to implement scalable AI solutions that drive innovation in our training programs. If you are passionate about harnessing the power of AI to transform the sports industry and have a strong foundation in NLP and machine learning, this is the perfect opportunity for you to make an impact. Responsibilities - Design and implement LLM systems tailored to the needs of athletes and coaches. - Optimize retrieval-augmented generation processes to improve the quality and relevance of AI-generated content. - Collaborate with cross-functional teams to define AI strategies and ensure alignment with business goals. - Conduct research on cutting-edge AI methodologies and integrate them into existing systems. - Monitor and evaluate system performance, making data-driven adjustments as necessary. - Mentor junior team members and help cultivate a culture of innovation within the department. - Document system architecture, processes, and best practices for future reference and team knowledge sharing.