Machine Learning Engineer (Middle)

You are a Machine Learning Engineer with strong programming skills in Python and JavaScript/TypeScript (Node.js), comfortable applying modern ML practices to real-world product workflows. This is a hybrid role at the intersection of AI/ML engineering and software development - you'll be working not just with models, but also on the systems that bring them to life in production environments.

You bring hands-on experience building and deploying intelligent systems, with a solid understanding of modern ML techniques - and a bonus if you enjoy exploring the theory behind them. While the current focus is on NLP and LLMs, your expertise may also be applied to potential projects involving classical ML or other applied AI areas, depending on your strengths and the direction of future product initiatives.

You're excited to work at the intersection of developer tools, AI infrastructure, and LLM-based workflows, helping shape how the next generation of engineers write, refactor, and understand code. You understand that building intelligent systems isn't just about models - it's about designing resilient APIs, clean architectures, and maintainable codebases that support evolving user needs.

You actively follow the latest developments in LLM architectures, agent frameworks, and retrieval techniques, and know how to translate those ideas into production-ready systems. In the early stages, you'll spend more time working on platform-level features and backend infrastructure - and gradually grow into building increasingly complex agent-driven and AI-first functionality as the product evolves.

Na jakie zadania (obowiązki)?

What you'll do:

• In the early stages, focus on building robust backend APIs and microservices to support key business logic - including authentication, subscriptions, usage tracking, and user management.

• Gradually shift into designing and implementing the core agent and LLM workflow logic, including memory, context-awareness, multi-agent collaboration, tool calling and many more.

• Collaborate on integrating multiple LLM providers (OpenAI, Claude, Gemini, OpenRouter, etc.) into the routing system, supporting structured output and external tool interactions

• Work with vector databases (Qdrant, Pinecone, Chroma, Faiss) and use efficient RAG techniques for code-aware reasoning and search

• Partner with frontend and backend engineers to create seamless user-facing developer experiences across our products

• Contribute to technical design discussions and help evolve the architecture to support both AI workflows and broader platform needs

• You may also be involved in other ML or AI-related product initiatives, depending on the direction of parallel projects

• Depending on your skills and interests, you may also take part in other AI/ML initiatives not limited to NLP - including classical ML projects and adjacent product experimentation

You're at your best:

• A self-starter with great problem-solving skills; able to work independently and collaboratively

• Solid Computer Science background and strong engineering mindset

• A technologist at heart, always eager to learn and explore new frameworks, tools, and techniques

• Works effectively both independently and in collaborative team settings

• A strong communicator who knows how to listen, reason, and find common ground in discussions

• Bring a product-oriented mindset, always mindful of the end-user impact of technical decisions

• Possess strong empathy and emotional intelligence, capable of building trust and healthy team dynamics

• Enjoy sharing knowledge, mentoring junior colleagues, and contributing to a strong engineering culture (for Senior)

Your engineering Superpowers:

• Commercial experience 3+ years of commercial experience working in ML/AI product environments

• Strong programming skills in Python and JavaScript/TypeScript (Node.js)

• Proven ability to write clean, maintainable, and well-tested code (unit testing as part of your process)

• Applied experience with NLP/LLM models, especially transformer-based architectures

• Proficiency in designing, executing, and maintaining AI/ML systems and solutions in a production environment

• Hands-on experience building and optimizing LLM-based workflows: agent and multi-agent systems, context-awareness, persistence, memory, prompt engineering, structured output, tool use

• Familiarity with agentic and agent orchestration frameworks (LangGraph, LangChain, LlamaIndex, AutoGen, PyDantic AI, smolagents, etc.)

• Experience working with LLM provider APIs (e.g. OpenAI, Anthropic, Gemini, OpenRouter) for prompt execution, tool calling, and structured outputs

• Experience working with vector databases, RAG strategies, and hybrid search techniques

• Exposure to observability tools (LangSmith, Langfuse, Traceloop, MLFlow, etc.) for evaluating and improving AI behavior in production

• Good knowledge of SQL/NoSQL databases and data modeling

• Strong software engineering fundamentals - including GitOps, CI/CD, Docker; familiarity with Kubernetes is a plus

• Prior use of AI code editors or developer productivity tools

Nice to have:

• Solid theoretical understanding of Machine Learning fundamentals

• Bachelor's degree (or higher) in Computer Science, Machine Learning, or a related field

• Demonstrated initiative through self-driven ML/AI projects or prototypes is a strong advantage

• Achievements or participation in Kaggle competitions, or real-world applied ML problem solving

• Experience building agent systems for code (e.g. working with or inspired by Cursor, Continue, Windsurf, bolt.new, v0.dev)

• Experience with streaming architectures or messaging systems (Kafka, etc.)

• Contributions to open-source ML frameworks or AI infrastructure tools

• Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX (autograd, dynamic/static computation graphs)

• Experience with classical ML libraries such as scikit-learn, and gradient boosting frameworks like XGBoost, LightGBM, or CatBoost

Jakie warunki i bonusy?

Conditions and bonuses:

• A unique opportunity to lead and shape the launch of a new brand

• Work in a dynamic and innovative Tech AI environment

• Competitive salary package

• Generous budgets for initiatives and growth opportunities

• Vacation days 25, additional days off, and sick leave

• Flexible working options: remote or hybrid in one of our offices

• Clear paths for professional and career growth