synvert Portugal(Recruiting)

Role Description We're looking for a Software Engineer who has already crossed that line. Someone for whom AI tools are a first-class part of daily engineering practice, and who brings the same instinct to designing, leading, and shipping AI-powered systems for our clients. Concretely, you will: - Build LLM-based applications, agentic workflows, and AI-powered features that are production-ready and maintainable. - Implement retrieval pipelines, tool integrations, and orchestration logic within agentic systems. - Contribute to evaluation and monitoring practices for AI systems in production, helping measure and improve output quality over time. - Integrate AI capabilities into existing products and architectures with attention to reliability and performance. - Collaborate with senior engineers and product stakeholders to understand use cases and translate them into working solutions. - Use AI coding tools (Claude Code, Cursor, Copilot, or similar) as a meaningful part of your development workflow, not just occasionally. - Stay current with the AI tooling ecosystem and contribute informed perspectives on what's worth adopting. - Grow your scope progressively, with team support, toward end-to-end ownership of AI system components. Qualifications - 2+ years of experience building production software systems, with exposure to AI or ML-driven features. - Working understanding of modern AI concepts: LLMs, embeddings, retrieval systems, and at least the basics of agentic design patterns. - Solid engineering fundamentals (Python and/or TypeScript/Node.js) and experience contributing to backend services in production. - Ability to implement well-scoped solutions from clear technical direction, and to flag when scope or approach needs discussion. - Thinks about whether an agent or model-driven approach could fit a problem before defaulting to traditional code. - Genuine curiosity about how AI systems work, not just as a user, but as someone who wants to understand and improve them. - Growth mindset: you're honest about what you don't know and proactive about closing the gap. - Comfort with day-to-day ambiguity: you can make progress even when the full picture isn't clear. - Pragmatic instincts: you'd rather ship something good than stall waiting for something perfect. - Clear communicator who asks good questions and knows when to seek input. Requirements - Has used AI coding tools (Cursor, Claude Code, Copilot, etc.) as a meaningful part of a real project and can speak to what that changed. - Has built a small agentic workflow, LLM-powered tool, or AI-assisted automation, even as a side project or exploration. - Familiar with evaluation and testing approaches for AI outputs (prompt testing, output comparison, basic regression). - Has experimented with frameworks or tools in the AI ecosystem: LangChain, LlamaIndex, LangGraph, Anthropic tool use, OpenAI function calling, or similar. - Has replaced something they did manually with an AI-driven workflow and can describe what they learned. - Reads about AI developments with genuine interest and forms opinions about them. Benefits - Access to frontier AI as part of your daily work: latest models and tooling available without an approval gauntlet. - A team that already works this way. You won't be the only one rethinking how engineering gets done. - Agile Company Culture and the Best Team. - Global Projects & Opportunities. - Social Events & Team Building. - Continuous Development: Training & Development, Growth Opportunities. - Flexible Working: Remote Friendly Culture. - Great Equipment & Tools. - Flexible Benefits. - Extra Days Off. - Health Insurance. Hiring Process Our process is direct and designed to respect your time: - PX interview - Technical interview - Final conversation - Offer * The technical interview focuses on real problems, not algorithmic puzzles. We evaluate candidates holistically. If you don't meet every requirement listed above, apply anyway. We care more about how you think and work than whether your CV matches a checklist. See you on the other side!

Role Description We're looking for a Senior Software Engineer who has already crossed that line. Someone for whom AI tools are a first-class part of daily engineering practice, and who brings the same instinct to designing, leading, and shipping AI-powered systems for our clients. Concretely, you will: - Lead the design and implementation of multi-step agentic pipelines, LLM-based applications, and AI-powered workflows, taking end-to-end responsibility from architecture through to production. - Define and own evaluation frameworks and feedback loops for AI systems in production: prompt regression suites, output quality monitoring, and continuous improvement cycles. - Make architectural decisions on AI system design (model selection, orchestration strategy, retrieval approach, latency and cost trade-offs) and explain the reasoning clearly to engineering and product stakeholders. - Identify high-impact AI opportunities in complex client environments, translate them into actionable technical proposals, and lead their execution. - Integrate modern AI capabilities (agentic orchestration, structured generation, tool use, model routing) into existing architectures with a clear eye on reliability and maintainability. - Set the bar for engineering practice on the team: code quality, observability, evaluation rigour, and responsible AI deployment. - Use AI coding tools (Claude Code, Cursor, Copilot, or similar) as a primary part of your development workflow, not an occasional aid, and help the team level up in how they work. - Stay at the frontier of the AI tooling ecosystem and bring informed opinions on what's worth adopting into practice. Qualifications - 5+ years of experience building production software systems, with at least 2 years working on AI or ML-driven features in production. - Deep understanding of modern AI concepts: LLMs, embeddings, retrieval systems, agentic design patterns, evaluation frameworks, and observability for AI systems. - Strong engineering fundamentals (Python and/or TypeScript/Node.js) and a track record of building reliable, scalable backend services. - Demonstrated ability to lead technical decisions (architecture, trade-offs, technology selection) and communicate them clearly across engineering and product. - Experience designing and owning systems under real-world constraints: performance, cost, reliability, and long-term maintainability. - Ability to take ambiguous, open-ended problems and drive them to well-scoped, shippable solutions with minimal direction. - High agency: you operate autonomously, make decisions, and flag clearly when you need input. - Defaults to asking "can an agent or model handle this?" before reaching for a traditional approach, and knows when the answer is no. - Pragmatic over perfectionist: you know when "good enough in production" beats "perfect in theory," and when it doesn't. - Low ego, high standards: you hold the bar without holding the room hostage. Requirements - Has shipped real products using AI-assisted coding workflows and can speak concretely to what changed in their engineering process. - Has designed and operated LLM evaluation harnesses, red-teaming pipelines, or output regression frameworks at scale. - Active user of (or contributor to) the emerging AI tooling ecosystem: MCP, agent protocols, model routers, LLMOps tooling. - Experience with the full prompt engineering lifecycle in production: versioning, A/B testing outputs, and monitoring for drift. - Has rebuilt an internal workflow or tool they previously did manually by replacing it with an agentic system, and can describe what they learned. - Comfortable reading frontier AI research and forming opinions on what's worth adopting versus what's hype. - Experience mentoring or technically guiding other engineers on AI system design. Benefits - Access to frontier AI as part of your daily work: latest models and tooling available without an approval gauntlet. - A team that already works this way. You won't be the only one rethinking how engineering gets done. - Agile Company Culture and the Best Team. - Global Projects & Opportunities. - Social Events & Team Building. - Continuous Development: Training & Development, Growth Opportunities. - Flexible Working: Remote Friendly Culture. - Great Equipment & Tools. - Flexible Benefits. - Extra Days Off. - Health Insurance. Hiring Process Our process is direct and designed to respect your time: - PX interview - Technical interview - Final conversation - Offer * The technical interview focuses on real problems, not algorithmic puzzles. We evaluate candidates holistically. If you don't meet every requirement listed above, apply anyway. We care more about how you think and work than whether your CV matches a checklist. See you on the other side!

Role Description As a Data Engineer, you will be a key part of the team, enabling and facilitating a data-driven culture for internal and external clients and using advanced data pipelines to generate insights on where to go. - Collaborate with a talented international team of engineers, architects, and cloud experts to design and implement data-driven solutions from concept to deployment. - Translate business needs, user stories, and technical requirements into scalable and efficient data warehouse solutions. - Communicate your technical decisions to team members and stakeholders using clear, accessible language. - Proactively suggest and implement improvements to existing processes, engage in technical discussions, and lead code reviews, providing constructive feedback and innovative solutions. - Be involved in the entire development lifecycle of data pipelines and warehouse architectures, applying critical thinking and leveraging advanced tools and methodologies to ensure impactful results. - Embrace a bold and resilient mindset, experimenting with new approaches and learning from challenges to achieve success more often. Qualifications - At least 2 years of experience in similar positions - Hands-on experience with Databricks - Solid understanding of cloud environment and their integration with Data Warehouse solutions - Strong Data Warehouse experience with diverse data models - Experience with Python - Experience with SQL - Fluent in English and strong communication skills Requirements - Resilience and the ability to navigate change and adapt - Experience working in a fast-paced start-up environment - Experience developing solutions using Generative AI, LLMs, or agentic architectures - Experience leveraging AI-powered SDLC workflows for both product and engineering tasks Benefits - Agile Company Culture and the Best Team - Global Projects & Opportunities - Social Events & Team Building - Continuous Development - Training & Development - Growth Opportunities - Flexible Working - Remote Friendly Culture - Great Equipment & Tools - Flexible Benefits - Extra Days Off - Health Insurance

Role Description Plugging into an LLM API is not the same as building an AI system that works in production under real-world pressure. The gap between those two keeps widening, and the engineers who can bridge it are becoming the most valuable people on any team. These are the people who understand model behaviour, evaluate rigorously, and design systems where AI is a load-bearing component. We're not looking for engineers who have just discovered GenAI. We're looking for engineers whose craft is AI: who reason about model behaviour the way strong software engineers reason about distributed systems, who build evaluations before they build features, and who can tell you exactly why a model is failing and how to fix it. This role is for the engineers our clients rely on when the stakes are highest: when the AI has to actually work, not just demo well. Concretely, you will: - Design and build production AI systems where model behaviour is load-bearing: retrieval architectures, agentic workflows, multi-model orchestration, structured generation pipelines. - Own the evaluation methodology end-to-end. Define what "good" means for a given system, build regression suites, run offline and online experiments, and track drift and degradation once it's in production. - Make model and architecture decisions with real trade-offs in mind (latency, cost, accuracy, data sensitivity, operational complexity). - Debug AI systems at the model level: prompt behaviour, retrieval failure modes, tokenisation edge cases, and generation drift. - Contribute to or drive LLMOps infrastructure: inference serving, caching, monitoring, experimentation, and cost control. - Translate ambiguous business problems into concrete AI system designs, with clear reasoning about what's feasible today versus what's still speculative. - Stay at the frontier of AI/ML research: read papers, evaluate techniques critically, and bring what's genuinely useful into practice. - Use AI coding tools (Claude Code, Cursor, Copilot, or similar) as a natural part of your workflow. Qualifications - 3+ years as a software engineer and/or at least 2+ years of substantial production work on AI/ML or GenAI systems. - Deep understanding of modern AI systems: LLMs, embeddings, vector search, retrieval pipelines, agentic architectures, evaluation frameworks, and production observability for AI. - Working knowledge of classical ML fundamentals: supervised learning, evaluation metrics, overfitting, data distribution shifts. - Strong engineering fundamentals in Python (primary), ideally with working comfort in TypeScript/Node.js. - Proven ability to design and own evaluation methodology, not just use existing frameworks. - Can reason about model behaviour at depth: why a system failed, how to measure improvement, how to build systems that degrade gracefully. - Comfortable at the boundary between research and production. - High agency and technical leadership. - Pragmatic about when AI is the right tool, and when it isn't. - Low ego, high standards. Requirements - Hands-on experience with fine-tuning, LoRA/QLoRA, DPO, or similar adaptation techniques. - Has built evaluation harnesses from scratch: LLM-as-judge pipelines, regression suites, red-teaming setups, or human-in-the-loop eval workflows. - Experience deploying AI systems under serious latency or cost constraints. - Contributions to or deep familiarity with the open-source ML ecosystem. - Experience with the full LLMOps lifecycle: model routing, inference serving, caching, monitoring for drift, cost attribution. - Can discuss specific papers with informed opinions. - Has worked on classical ML problems and can speak to what changes with GenAI and what doesn't. - Active contributor to the AI tooling or research community. - Has shipped real products using AI-assisted coding workflows. Benefits - Problems worth your depth: work on AI systems that are load-bearing, not decorative. - Access to frontier AI as part of your daily work: latest models, open-source weights, and tooling available without an approval gauntlet. - A team that engages at depth: peers to push back on you, and engineers to mentor. - Agile Company Culture and the Best Team. - Global Projects & Opportunities. - Social Events & Team Building. - Continuous Development: Training & Development, Growth Opportunities. - Flexible Working: Remote Friendly Culture. - Great Equipment & Tools. - Flexible Benefits. - Extra Days Off. - Health Insurance. Hiring Process Our process is direct and designed to respect your time: - PX interview - Technical interview - Final conversation - Offer The technical interview focuses on real problems, not algorithmic puzzles. We evaluate candidates holistically. If you don't meet every requirement listed above, apply anyway. We care more about how you think and work than whether your CV matches a checklist. See you on the other side!

Role Description As a DevOps Engineer, you will work closely with cross-functional teams, including software architects, data strategists, and cloud experts, to ensure full-stack alignment in our Cloud Native Acceleration projects. Your role will be pivotal in designing, implementing, and optimising cloud strategies (Single, Hybrid, Multi-Cloud), data strategies, software architecture, and modern DevOps practices (DevOps 2.0). Qualifications - At least 2 years of experience in DevOps, cloud technologies, and development in cloud-native environments - Strong knowledge of Google Cloud Provider (GCP) and services - Experience with containers and container orchestration (e.g. Docker, Kubernetes) - Experience in automation tools (e.g. Terraform, Ansible) and CI/CD pipelines - Hands-on experience with scripting for automation (e.g. Python, Bash) - Familiarity with monitoring and logging tools (e.g. Prometheus, Grafana, ELK) - Basic understanding of cloud security practices (IAM, secrets management) - Ability to work in agile methodologies and with cross-functional teams - Ability to speak and write in English Requirements - Experience and awareness of other Cloud provider services and interest in exploring, if needed - Experience working in a fast-paced start-up environment - Public speaking and blog writing interest and spreading our culture and skills - Open-source contributions - Experience developing solutions using Generative AI, LLMs, or agentic architectures - Experience leveraging AI-powered SDLC workflows for both product and engineering tasks Benefits - Agile Company Culture and the Best Team - Global Projects & Opportunities - Social Events & Team Building - Continuous Development - Training & Development - Growth Opportunities - Flexible Working - Remote Friendly Culture - Great Equipment & Tools - Flexible Benefits - Extra Days Off - Health Insurance

Role Description As a Software Engineer at synvert, you'll be part of a smart and supportive cross-disciplinary team, including software architects, data strategists, and cloud experts, to ensure full-stack alignment in our projects. Qualifications - At least 3 years of working experience - Experience/Interest in working with Ruby on Rails - A degree in software engineering or proven experience in the field - Comfortable communicating in English (written and spoken) - Able to work outside your comfort zone and still get things done Requirements - Experience developing solutions using Generative AI, LLMs, or agentic architectures (Nice-to-Have) - Experience leveraging AI-powered SDLC workflows for both product and engineering tasks (Nice-to-Have) - Experience or interest in DevOps and Infrastructure (e.g. CI/CD pipelines, Docker/Containers, Cloud, etc) (Nice-to-Have) - Experience or interest in Data Engineering (Nice-to-Have) Benefits - Agile Company Culture and the Best Team - Global Projects & Opportunities - Social Events & Team Building - Continuous Development - Training & Development - Growth Opportunities - Flexible Working - Remote Friendly Culture - Great Equipment & Tools - Flexible Benefits - Extra Days Off - Health Insurance Hiring Process One of the perks of being a part of synvert is that you’ll join a global team of over 500 experts from Germany to the UK, Croatia and even the UAE. As a member of our team, you’ll have the opportunity to work on cutting-edge cloud native and AI solutions, leveraging synvert's extensive industry knowledge and technical expertise. Join us and be at the forefront of delivering excellence in DevOps, Data, and Digital Products! And even if you don’t think you meet 100% of the requirements, submit your application. We’re always happy to meet new people! Here’s what you can expect from our application process: - PX Interview - Tech Challenge - Tech Interview - Final Interview - Offer