AI Engineer (m/f/d) at BIT Capital GmbH

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

22 Sep, 26

Salary

0.0

Posted On

24 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Python, SQL, AWS, Databricks, LLM, Agentic AI, LLMOps, Data Science, Machine Learning, CI/CD, Prompt Engineering, Model Evaluation, Software Engineering, Cloud Infrastructure, Retrieval Augmented Generation, System Design

Industry

Financial Services

Description

YOUR ROLE Your Role & How we work We are hiring an AI Engineer (m/f/d) to holistically develop BIT Capital's LLM and agentic Artifical Intelligence capabilities: from the data science that turns data into investment signals, to the engineering that powers our financial intelligence agents. A strong initial focus is the build-out of “Aion” - our agentic research platform - that has first-class access to our data, models, and tools and is already in mainstream production, used by the investment team every day. Beyond, your mandate covers everything LLM- and agent-related across BIT, from research products for the investment team to internal tools in Sales, Marketing, Operations, and client-facing experiences. You work autonomously, from first scoping to shipping, and you self-select the use cases and projects where you can have the most impact. You report to our Carlos Bielsa (Chief AI Officer & Managing Partner) and work side by side with Vlad Gheorghe, our second AI Engineer, acting as our internal expert as the frontier of AI moves. Our stack is Python, SQL, AWS, and Databricks, with models and agentic frameworks from the leading AI labs (Anthropic Managed Agents, Codex). Your priorities in this role will thus include: **Agentic systems behind Aion.** Build and evolve the agentic systems that power Aion, along with the research skills and Python sidecars that agents execute. **Data and tool integrations.** Build the integrations that give agents performant, safe access to BIT's data and capabilities. **Evaluations and model selection.** Build automated and human-in-the-loop evaluations, regression tests, and monitoring that track accuracy, hallucination rates, latency, cost, and model uncertainty. Benchmark and select the right model per task, including open-source options. **LLMOps at scale.** Establish the LLMOps practices that keep these systems reliable at scale: versioning of prompts, configs, and models; CI/CD; error handling; fallbacks; and cost control. **Frontier into production.** Stay at the frontier of LLM and agentic AI techniques and bring new ideas into production rather than leaving them as experiments. Investment signal pipelines. Design and ship LLM-based extraction pipelines (prompts, retrieval, model selection, evaluations) that turn text and data into investment scores, alerts, signals, and insights reliable enough to drive decisions. Agentic systems behind “Aion”. Build and evolve the agentic systems that power Aion, along with the research skills and Python sidecars that agents execute. Data and tool integrations. Build the integrations that give agents performant, safe access to BIT's data and capabilities. Evaluations and model selection. Build automated and human-in-the-loop evaluations, regression tests, and monitoring that track accuracy, hallucination rates, latency, cost, and model uncertainty. Benchmark and select the right model per task, including open-source options. LLMOps at scale. Establish the LLMOps practices that keep these systems reliable at scale: versioning of prompts, configs, and models; CI/CD; error handling; fallbacks; and cost control. Frontier into production. Stay at the frontier of LLM and agentic AI techniques and bring new ideas into production rather than leaving them as experiments. The experience you bring You have a solid data science and engineering background, with at least 2 years building software, data systems, or ML in production. You adopt the mindset of a product-minded software engineer. You have shipped your own apps and agents and can point us to a public project, repo, or product. You are fluent with the current LLM and agentic frontier, with hands-on experience using leading agentic systems (Claude Code, Anthropic Managed Agents, Codex, open-source agentic loops). You have strong Python and SQL skills with clean engineering practices, plus cloud infrastructure experience (AWS strongly preferred). You bring production discipline for probabilistic systems: evaluations, regression tests, monitoring, reproducibility, and cost awareness. A scientific approach to turning LLMs into signals and scores (ground-truth datasets, train/test/iterate) is a plus; a data science or ML background helps but is not required. You have a genuine interest in investing, or at minimum a strong drive to learn about our domain quickly and deeply, paired with sharp general intelligence and clear systems thinking. 2 reasons why you should not apply You mainly use AI tools for your own productivity and have not yet taken systems into production. This role is about building and shipping AI that the whole firm relies on, so prior production experience matters here. You are looking for a finished, stable AI stack to operate and maintain. Our systems evolve quickly as the frontier moves, so you will spend much of your time building and reshaping them rather than keeping a fixed setup running. Your mindset You're an "AI Native": you use agentic AI as core infrastructure in how you work, not as a novelty. You're a "Builder": you ship working systems and improve them continuously rather than leaving ideas as experiments. You blend data science rigor with engineering pragmatism, with care for correctness, reliability, and cost. You're passionate about AI, emerging technology, and investing, and you pair technical depth with strong product judgment. You manage for outcomes, own problems end-to-end, and communicate clearly on progress, tradeoffs, and risk. YOUR PROFILE What we offer Work at the frontier of applied AI in asset management, with petabytes of data and the most cutting-edge tools. Real technical direction and ownership from day one, as the internal expert while the frontier moves. Contribute to a unique success story in European asset management, surrounded by the most knowledgeable experts in the industry. A motivated, highly international team (15 nationalities) that enjoys getting together in person in Berlin and strongly supports each other. Flat hierarchies and direct interaction with our management team. Competitive compensation, relocation, and visa support if needed. OUR BENEFITS CONTACT US & DISCLAIMER. CONTACT INFORMATION About us BIT Capital combines the expertise of an industry leader in asset management with the agility of a young tech company. With its investment products, BIT Capital enables investors to participate in the future growth of the technology-driven world. The guiding principle is always to be “Ahead of the Curve" - anticipating technology mega trends and identifying the major investment opportunities of the future before they emerge. The BIT Capital team expertly navigates a constantly evolving landscape of complex technology sectors, identifying tomorrow's technology leaders at an early stage on behalf of its investors. The experienced team currently consists of around 40 professionals, including an investment team of 20 financial market experts, sector specialists, data engineers and software developers. The flagship fund, BIT Global Technology Leaders, has received multiple performance awards and is one of the highest performing equity funds in Europe. As part of our recruitment process, we use digital tools that incorporate AI-supported functionalities to structure and review application materials and to support the selection process. The results are used solely as support for the initial screening and are always reviewed by responsible personnel. Decisions regarding an offer of employment or a rejection are not made solely by automated means.

Responsibilities

Develop and scale agentic AI capabilities and LLM-based research platforms to generate investment signals. Establish LLMOps practices and build integrations to provide AI agents with safe, performant access to financial data.