Key responsibilities in your new role:You’ll work on Agentic AI projects—building and evaluating LLM-powered agents that plan, reason, use tools, and act in dynamic environments—without being assigned to a single team. You will work hands-on with Machine Learning, big data, and modern AI orchestration to ship real features and experiments. Expect a diverse, inclusive, and collaborative setting with mentorship, hack days, and competitive pay.
- Design, implement, and scale agentic LLM solutions: Focus on planning, memory, tool-use, multi-step reasoning, and retrieval-augmented generation (RAG) with safety guardrails
- Expand your horizons: Prototype autonomous agents that integrate with APIs, vector databases, and knowledge bases to accomplish real tasks across user workflows
- Data is everything: Collect, clean, curate, and analyze datasets, as well as instrument telemetry to drive iterative improvements in reliability, robustness, and latency
- Experience research: Participate in the full lifecycle of ML/LLM systems, including evaluation, optimization, refinement, and product ionization (experiments, A/B tests, benchmarking, and evaluation harnesses)
- Reliable work: Develop dashboards and simple UIs to monitor agents, visualize experiments, and support user feedback loops
- Lifelong learning: Explore and experiment with agentic architecture, LLM orchestration frameworks (e.g., LangChain/LlamaIndex), embeddings, and retrieval systems to foster innovation
- Being strong together: Collaborate with mentors and cross-functional stakeholders, while engaging in innovation events, demo sessions, and hack days to share learnings and spark new ideas
Your Profile
Qualifications and skills to help you succeed:
- Study field: Currently enrolled in a master’s degree (or equivalent) in Computer Science, Artificial Intelligence, Data Science, or related fields
- Personality: Meticulous and inquisitive nature with high professional integrity and maturity
- Way of working: Self-motivated team player who can work independently and communicate clearly
- Skills: Solid programming skills in Python and familiarity with Machine Learning fundamentals; curiosity about LLMs and agentic architecture
- Technical abilities: Proficiency in problem solving, prompt/design experimentation, and AI algorithm research; comfort with data analysis on big data
- Knowledge: Resourcefulness in learning new tools and frameworks: embeddings and vector databases, LLM orchestration (e.g., LangChain), data tools (Pandas/Spark), and experimentation
- Experience: Knowledge in reinforcement learning or planning, knowledge bases, Docker/Kubernetes, and cloud platforms would be a plus
Please send us your CV in English so we can get to know you better.
