LanguageWire is a global leader in language and content services, dedicated to helping brands achieve their full global potential. Since 2000, we’ve evolved and built unique technology that enables seamless multilingual communication. Our worldwide network of language experts, together with our intuitive online platform, empowers brands to translate and manage multilingual content effortlessly.    The role you’ll play  You will help improve our existing ML-based products and build new features and services using LLMs. A core part of the role is context engineering — designing the right inputs, retrieval strategies, and prompt structures to get the best possible output from LLMs in production. You'll also contribute to our software engineering efforts when needed, bridging the gap between ML and software engineering on the team.    The team you’ll be a part of    You will work in the Machine Learning Team, where we build LanguageWire's machine translation (MT) and adjacent services to monitor and predict its performance. The team consists of ML Engineers, Analytics Engineers, and Full Stack Software Engineers. You'll collaborate across these disciplines daily, and we value people who can move fluidly between ML experimentation and production engineering. You'll report to the team leader.      If you want to make a difference, make it with us by…    - Context engineering LLM-based translation workflows — designing retrieval strategies, prompt structures, and input pipelines that maximize output quality.  - Productionizing the prototypes that prove their value.  - Staying current with LLM research & tools, bringing relevant findings back to the team.  - Constructing and evaluating experiments to measure what actually works.    In one year, you’ll know you were successful if…    - We have made meaningful progress toward agentic MT — where translation workflows can reason, self-correct, and orchestrate multiple steps autonomously  - Our LLMOps practices are mature enough to deploy, monitor, and iterate on LLM-based services with confidence  - Our ways to evaluate MT have improved, applying techniques such as LLM-as-a-judge.  - We can offer even better personalized MT for our customers using LLMs.        Desired experience and competencies   What you’ll need to bring  - 2–3+ years of relevant experience from areas such as ML engineering, AI engineering, or software engineering with a strong ML focus  - Hands-on experience with LLMs and prompt/context engineering — either professional or through substantial personal projects.  - Experience with best practices and building production grade systems in Python. Knowledge of other programming languages is considered a bonus.  - Experience constructing and evaluating experiments.  - A strong analytical mindset — you think in terms of hypotheses, baselines, and measurable outcomes  - A keen interest in the rapidly evolving LLM space and tooling.      This will make you stand out     Experience with:    - Context engineering — designing retrieval, chunking, and prompt strategies for production LLM systems  - Building agentic products or prototypes  - Training or fine-tuning ML models  - LLMOps — deployment, monitoring, evaluation, and iteration of LLM-based services  - Building production-grade, scalable software systems    Our perks    - Enjoy flat hierarchies, responsibility and freedom, direct feedback, and room to stand up for your own ideas  - Internal development opportunities, ongoing support from your People Partner, and an inclusive and fun company culture  - International company with over 300 employees. Offices in Copenhagen, Aarhus, Stockholm, Varberg, London, Leuven, Lille, Paris, Munich, Hamburg,, Sophia Antipolis, Atlanta, Finland and Valencia  - For this role, we have a full-time FlexiWire@home option for remote work. Of course, you are always welcome at the office to collaborate and connect with your colleagues.  - We take care of our people and initiate many social get-togethers from Friday Bars a to Summer or Christmas parties. We have fun!  - Excellent location in cool and modern offices in the city center, with a great rooftop terrace and a view over the Town Hall Square  - Private health insurance  - Working in an international environment—more than 20 different nationalities  - A dog friendly atmosphere  - Big kitchen with access to organic fruits, nuts and biscuits and coffee.  - Social area and game room (foosball table, darts, and board games)  - Bike and car parking        About LanguageWire   At LanguageWire, we want to wire the world together with language. Why? Because we want to help people & businesses simplify communication. We are fueled by the most advanced technology (AI) and our goal is to make customer's lives easier by simplifying their communication with any audience across the globe.  Our values drive our behavior  We are curious. We are trustworthy. We are caring. We are ambitious.  At LanguageWire, we are curious and intrigued by what we don’t understand. We believe relationships are based on honesty and responsibility, and being trustworthy reinforces an open, humble, and honest way of communicating. We are caring and respect each other personally and professionally. We encourage authentic collaboration, invite feedback and a positive social environment. Our desire to learn, build, and share knowledge is a natural part of our corporate culture.     Working at LanguageWire — why we like it:   “As a Senior Full Stack Engineer at LanguageWire, my day is dynamic and collaborative, particularly within the Machine Learning team. I work closely with Machine Learning Engineers to integrate advanced AI capabilities into our web applications and APIs, aiming to enhance customer experiences through innovative features. I also collaborate with other development teams to ensure seamless integration and alignment of our technology solutions, fostering continuous learning and driving the evolution of LanguageWire's technology.”  (Cristian, Senior Full-Stack Engineer, Poland)  Yes, to diversity, equity & inclusion  In LanguageWire, we believe diversity in gender, age, background, and culture is essential for our growth. Therefore, we are committed to creating a culture that incorporates diverse perspectives and expertise in our everyday work.  LanguageWire’s recruitment process is designed to be transparent and fair for all candidates. We encourage candidates of all backgrounds to apply, and we ensure that candidates are provided with an equal opportunity to demonstrate their competencies and skills.  Want to know more?  We can’t wait to meet you! So, why wait 'til tomorrow? Apply today!  If you want to know more about LanguageWire, we encourage you to visit our website!

Senior AI/ML Engineer Job Summary: Join our dynamic and innovative team at Alter Domus as an AI & ML Engineer, where you will play a pivotal role in designing, building, and maintaining our AI platform and ML pipelines. Working closely with data analysts, data scientists, and various business stakeholders, you’ll ensure data is reliable, efficient, and accessible in a scalable manner. What You’ll Do: Build & Prototype - Assemble off-the-shelf LLMs (Azure OpenAI, AWS Bedrock, Anthropic Claude, Meta LLaMA APIs) into Alter Domus’ products and agentifying our workflows - Rapidly prototype in low-code/visual studios (Firebase Studio, v0, Lovable, etc) and with agentic frameworks (LangChain, AutoGen, MCP/A2A). - Automate & Integrate - Connect your assistants to real systems via MCP, APIs, webhooks and microservices. - Code with AI Helpers - Leverage GitHub Copilot, Cursor, Windsurf, Cline and Q Developer to accelerate script, API and workflow development. - Write unit/integration tests and help maintain our CI/CD pipelines. - Live Projects You’ll Continue to Develop - domusAI: An adaptive AI assistant platform for AD employees that learn user preferences through a dynamic memory management system. It provides personalized support via agentic LLM chats and integrated tool capabilities. The system features semantic document search, contextual understanding, MCP integration, custom agent creator, and secure information retrieval. - domiAI: Our flagship virtual assistant powered by an integrated data and AI platform that transforms client-specific information into vectorized knowledge bases and structured data repositories, enabling real-time generation of meaningful insights, comprehensive reports, search, analytics, and predictions through an intuitive conversational interface. - domusDocs: A document extraction engine to extract intelligence and process business documents leveraging advanced GenAI techniques - Working on Alter Domus Knowledge Based Agents and vectorizing our corpus of data - Multi-Agentic Framework: The architecture that lets specialized AI agents coordinate on complex workflows, build MCPs—your chance to see multi-agent collaboration in action. Learn & Collaborate - Learn: Work with an agile team of data scientists, engineers, and AI specialists to master cutting-edge techniques in data analysis, prompt engineering, and AI implementation. - Collaborate: Rapidly prototype solutions, iterate based on client feedback, and deploy customized solutions that address specific business challenges while continuously expanding your technical capabilities and domain expertise. Support & Iterate - Help define test plans, troubleshoot issues and tune prompts for accuracy and performance. - Gather feedback from business stakeholders and iterate quickly on your prototypes. Who You Are: - Tech Enthusiast: You’ve built at least three AI end-to-end projects deployed in production - Curious Learner: You’re eager to continue exploring new LLM services, low-code studios and agentic frameworks. - Collaborative Communicator: You explain technical ideas clearly in English and enjoy working closely with SMEs and teammates. - Adaptive: You are quickly able to adapt to the development and deployment Golden Path internal to Alter Domus - Self-Starter: You solve problems proactively using online resources, community forums—or even autonomous agents of your own creation. Technical Requirements: - 5+ years of total experience with relevant experience of 3+ years in Python and related data science libraries (NumPy, Pandas, PyTorch, TensorFlow) - Experience creating agentic AI workflows using Lang graph, Autogen, CrewAI or other agent libraries - Experience with vector databases (Pinecone, Qdrant, or similar) - Proficiency with REST APIs and microservice architecture - Familiarity with AWS cloud platform - Experience with containerization (Docker) and orchestration tools (Kubernetes) - Strong understanding of NLP concepts and LLM implementation - Experience with prompt engineering and fine-tuning language models

About Us We’re building the future of writing development. We believe that writing is thinking, and thinking changes life outcomes. Our mission is simple: help more students learn how to express ideas clearly, confidently, and independently. We believe AI should support learning, not shortcut it. That belief shapes everything we build: tools that strengthen skills, respect the role of educators, and keep students doing the hard (and important) work of thinking for themselves. We’re a fast-moving team working at the intersection of education, design, and AI — building things that are used in real classrooms, by real teachers, with real impact. If you care about craft, learning, and building technology that actually matters, you’ll feel at home here. About the Role We’re hiring an AI-Native Product Engineer to build and ship high-quality frontend products powered by modern AI workflows. This is not a traditional frontend role — you’ll use agentic AI systems (multi-step agents, tools, evals) to accelerate development, while still owning clean, production-ready code when it matters. Ideal for engineers who enjoy building fast, thinking independently, and working at the intersection of product, AI, and user experience. What You’ll Be Doing - Build and ship frontend features using React - Use AI workflows (agents, tools, evals) to accelerate product development - Design structured AI systems — not just prompt in chat - Step in to debug, refine, and own code when AI falls short - Integrate APIs (including LLM services) and collaborate with backend teams - Improve UX, especially around interactive and content-heavy workflows - Ship clean, maintainable, production-ready code What We’re Looking For Core Requirements - Strong React + JavaScript/TypeScript fundamentals - Able to build and ship features independently - Comfortable working with APIs and backend integration - Good product sense + UI/UX awareness - Clear communicator, able to work in a remote setup AI-Native Mindset - Experience using AI beyond basic prompting (agents, workflows, tools) - Understands how to make AI outputs structured, testable, and reliable - Knows when to rely on AI — and when to take over manually Bonus - Angular exposure (for existing systems) - Experience with rich-text editors (e.g. Lexical) - Interest in EdTech / learning products Candidates should be able to show: - GitHub / live projects / portfolio - AI workflows or systems they’ve built - Real examples of shipped work

• Build and maintain production-grade AI agents that take action across ANNA's clinical and operational workflows — scheduling, intake, RCM, clinical decision support — using LLM reasoning over our shared data ontology. • Design and implement tool use, state management, and failure handling for multi-agent workflows, ensuring reliability and quality in production. • Contribute to ANNA's MCP server suite, exposing core data and product surfaces — Salesforce, scheduling, RCM, HR — to AI agents in a standardized, composable way. • Implement and optimize RAG pipelines using pgvector, including embedding strategies and vector search, to surface relevant clinical history at the point of care. • Build and iterate on clinician-facing product surfaces in React, ensuring they integrate seamlessly with ANNA's AI agent backend and fit the real-world workflows of clinical teams. • Own features end-to-end: from backend API design in Django/FastAPI through to the front-end experience, with a high bar for quality at every layer. • Iterate quickly in the open with clinical and operational end users, incorporating feedback and shipping improvements with velocity. • Contribute to ANNA's clinical data ontology — the shared, specialty-agnostic data foundation that powers all AI agents, decision support, and outcome measurement across the platform. • Write clean, well-documented, HIPAA-compliant code and contribute to CI/CD pipelines and engineering best practices as the team scales. • Participate in architecture discussions and help make pragmatic build vs. buy decisions across the stack.