Head of Applied AI & Trading Systems (Iron Man Track) - Remote-Stelle

Deeter Analytics sucht Head of Applied AI & Trading Systems (Iron Man Track): Führungsniveau, und damit Verantwortung für ein Team oder einen Bereich. Die Stelle liegt im Bereich Weitere Stellen und ist vollständig remote. Das Unternehmen schränkt den Wohnort der Kandidatin nicht ein, Sie können sich also von überall bewerben.

Das Unternehmen hat keine Zahl veröffentlicht, das klärt sich im Gespräch. Zu den Arbeitszeiten stellt die Anzeige keine Bedingung.

Diese Stelle hat eine automatische Prüfung durchlaufen: Anzeigen, die eine ausländische Arbeitserlaubnis, Visa-Sponsoring, eine bestimmte Staatsangehörigkeit oder den Wohnsitz in einem genannten Land verlangen, kommen nicht auf die Liste.

The Gist Deeter Analytics is a private investment firm managing approximately $500M across public markets, alternatives, and private investments. We are small, profitable, and aggressive. We don’t have committees. We have capital, judgment, and a need for speed. We are hiring a technical builder to sit directly beside the CIO and architect the firm’s intelligence layer - the system that determines how we see the market . This is not a quant role. This is not a reporting role. This is not a research-for-research’s-sake role. This is a Human-in-the-Loop AI role : building the Iron Man suit - an exoskeleton that allows a human trader to process 100× more information without losing intuition, discretion, or speed. This role sits at the center of our trading advantage. The Problem Markets are noisy. Alpha comes from synthesis. We already have access to the data. The bottleneck is human bandwidth . Too much time is spent:

* Refreshing feeds

* Reading things that don’t matter

* Manually stitching together signal across sources

Latency kills insight. Insight kills latency. The Mission Build the nervous system of the firm. You will own the entire loop: Ingestion → LLM synthesis → Trader decision Your mandate is simple and brutal: Reduce time-to-insight across the entire trading operation. If you surface a signal 10 minutes faster than the street, we win. If you automate away four hours of reading, we double our capacity. What You Will Own 1. Information Pipelines (Kill the “F5” Key) You will design and maintain automated pipelines that ingest, clean, and normalize:

* News, filings, earnings calls, macro releases

* Social and sentiment data

* Alternative and proprietary datasets

Your job is to replace manual refresh workflows with push-based alerts that surface only what matters - mapped directly to:

* Watchlists

* Live positions

* Risk exposure

Reliability matters. Latency matters. Silence matters. 2. AI-Augmented Research (A Live Analyst on the Desk) You will deploy LLM-powered systems to summarize, extract, compare, and reason over:

* 10-Ks, 10-Qs, earnings calls, central bank minutes

* Sell-side research and internal notes

You will build “chat with our data” tools that allow traders to query proprietary research in natural language. Example: “Summarize the bear case from the last three earnings calls.” You will also create tools for fast discretionary back-testing: “How did this asset behave during the last three macro shocks of this type?” 3. Signal & Noise Filtering (Protect Attention) You will build the filters that decide what breaks through .

* Sentiment and relevance scoring

* Entity recognition that maps events to exposure

* Dashboards that surface regimes, anomalies, and dislocations - not vanity metrics

If something matters, it should scream. If it doesn’t, it should disappear. 4. Infrastructure, Security & Reliability (Own the Stack) You own the compute layer that runs the intelligence system.

* Cloud and/or local GPU infrastructure

* Vector databases and retrieval systems

* Data-privacy-first architectures (local models where required)

You choose the architecture. You ship what runs fastest and breaks least. Our proprietary data and strategies do not leak. Ever. Who You Are You Are a Founder-Hacker You have likely:

* Built your own projects

* Traded your own account

* Worked in a high-stakes startup, prop shop, or family office

You hate waiting for permission. You like tools that get used today . You Speak Python

* Python (Pandas, NumPy) is non-negotiable

* You have hands-on experience with:

* LLMs and RAG architectures

* Prompting, evaluation, and failure modes

* Vector databases (Pinecone, Milvus, FAISS, etc.)

* Orchestration (LangChain, Airflow, or custom agents)

* REST & WebSocket APIs (market data, news, internal tools)

* Lightweight internal UIs (Streamlit, Dash, Retool, etc.)

You know the difference between an LLM demo and a production system. You care about latency, hallucinations, and context windows. You Get Markets (Enough) You don’t need to be a portfolio manager - but you understand that: Financial data is messy, adversarial, and time-sensitive Narratives are not facts A 10-K is not a blog post You are comfortable making judgment calls under uncertainty. The Deeter Mindset Co-Ownership Mentality This is not a role for staff augmentation or support work. We are looking for a technical partner who thinks in outcomes, edge, and shared P&L - and wants to win in a competitive, adversarial market. We optimize for people who value: Pragmatism over theory You ship things that work by market open - not in six months. Discretionary empathy Human judgment is the final decision engine. Your systems amplify it; they do not replace it. Founder-level ownership You treat this system like your P&L depends on it - because it does. Why This Role

* Zero Latency You sit with the decision-makers. You push code. We use it the same day.

* Real Impact You are not optimizing ad clicks. You are building the intelligence engine for a $500M portfolio.

* Sovereignty No middle management. No reporting theater. Just results.

To Apply Do not send a cover letter. Send:

* A GitHub

* A project you’re proud of

* Or a short note on how you would architect a real-time news filter for a trading desk

We care about how you think and what you build. Originally posted on Himalayas