• Lead the design and development of full-stack machine learning and generative AI applications tailored to meet business needs. • Work closely with domain experts to translate business requirements into technical specifications and solutions. • Develop prototypes to assess technology readiness and guide the scaling of promising solutions into production-ready applications. • Provide technical guidance and mentorship to junior engineers and team members, fostering a collaborative environment. • Continuously monitor and optimize machine learning models and algorithms to improve performance and accuracy. • Collaborate with AI engineers, data scientists, software developers, UX/UI, and product managers to integrate AI solutions into existing products and develop new features. • Design and optimize data architecture and infrastructure to support efficient data storage, processing, and analysis. • Stay up-to-date with the latest trends and advancements in data science, machine learning, and defense technology. • Communicate findings and recommendations to technical and non-technical stakeholders through clear and compelling presentations and reports.

• Apply AI/ML modeling techniques in solving many types of problems • Develop and design algorithmic solutions for automation projects • Design, develop, and implement data science solutions to address defense-related challenges • Utilize advanced coding skills to manipulate, analyze, and visualize large and complex datasets • Develop and deploy machine learning models and algorithms to extract insights and make predictions • Collaborate with cross-functional teams to identify data requirements, gather relevant data, and define project objectives • Design and optimize data architecture and infrastructure to support efficient data storage, processing, and analysis • Stay up-to-date with the latest trends and advancements in data science, machine learning, and defense technology • Communicate findings and recommendations to technical and non-technical stakeholders through clear and compelling presentations and reports

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.