AI Engineer - AI procurement Startup Berlin

Cherry Ventures is supporting our portfolio with this hire

Location: Berlin, Germany 

Unfortunately at this stage we are unable to offer visa sponsorship for this position.

Intro

Forgent AI is on a mission to build AI products for better public procurement. We are looking for a talented Product Engineer to join our founding technical team in Berlin. You'll play a crucial role in shaping our product and technology and building reliable, impactful software for a domain where it truly matters.

The role

As an AI Engineer at Forgent AI, you will design, build, and optimize the sophisticated AI systems that differentiate our platform. This is a unique opportunity within an early-stage, mission-driven company to significantly shape our AI strategy and technical direction. You will tackle complex challenges applying your expertise in LLM fine-tuning, knowledge graphs, and vector search. Working closely with founders and other engineers, you'll have substantial ownership over key AI components, driving innovation from experimentation through to production-ready systems, and helping establish our AI development culture and best practices.

Your day-to-day

• Design, implement, and evaluate state-of-the-art AI models and systems, with a specific focus on LLMs and vector search/retrieval techniques (e.g., using pgvector or dedicated services, RAG).
• Develop and refine methods for extracting structured information and relationships from unstructured text data.
• Collaborate closely with product engineers, platform engineers, and founders to integrate AI components into the architecture and ensure alignment with user needs.
• Take ownership of specific AI components, managing data preparation, driving experimentation cycles, prototyping new approaches, and rigorously evaluating model performance.
• Stay abreast of the latest advancements in relevant AI fields and proactively identify opportunities to apply novel techniques to enhance our product.
• Contribute to technical design discussions, share research findings and experimental results with the team, and help establish best practices for AI development, deployment, and monitoring.

You should apply if you

• Are a skilled AI Engineer with 3+ years of professional experience building and deploying sophisticated AI/ML systems into production environments.
• Deep hands-on expertise with LLMs including model orchestration, agentic frameworks (e.g. LangChain, LangGraph, or similar frameworks), MLOps infrastructure (e.g. evals data), and advanced prompt engineering techniques (e.g. zero shot prompting, CoT, etc.). This should include:
  • Deep understanding of models, their context windows, and model behavior, and limitations (including practical limitations such as API call limits),
  • Experience with building agentic applications,
  • Collecting eval data in production and leveraging evals for system optimization.
• Have strong practical experience implementing and optimizing RAG systems, including:
  • Deep understanding of state-of-the-art embedding (e.g. contextual embeddings), retrieval (e.g. hybrid search), and chunking techniques,
  • Ability to implement and optimize vector searches (e.g., using Qdrant, Pinecone, or Weaviate),
  • Deep knowledge of model orchestration approaches and related infrastructure challenges to build scalable LLM-based and agentic solutions (e.g. ability to leverage frameworks such as Temporal).
• Can demonstrate successfully translating complex requirements or research concepts into practical, working AI solutions that deliver tangible results.
• Are an excellent communicator (written and verbal English) who thrives on deep collaboration, including code reviews and asynchronous design discussions.
• Are a hard worker with a strong sense of urgency, who thrives in a fast-moving, high-responsibility environment where direct communication is the norm and speed matters as much as quality.
• Are motivated by ownership, love to work in a fast-paced early-stage startup environment, are genuinely excited by our mission, and actively seek to improve yourself, your colleagues, and the team culture.
• Have impressive achievements from previous careers and from side projects — we’re excited to hear about these!

Nice to have

• Experience with fine-tuning LLMs for specific downstream tasks, utilizing frameworks like Hugging Face Transformers, PEFT, and similar libraries.
• Experience with MLOps best practices: deploying and monitoring models in production using common frameworks and tools (AWS preferred).
• Proficiency with Python and standard AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn, Pandas) necessary for model development and data manipulation.
• Practical experience with a wider range of machine learning models and paradigms beyond LLMs.
• Have proven experience designing, building, and querying knowledge graphs and graph databases (e.g., Neo4j, RDF stores, SPARQL) to represent complex relationships and domain knowledge.
• Good understanding of German (as our initial product context is German).
• Previous experience founding a company or working in a very early-stage startup.