Senior AI Engineer

Senior AI Engineer

Our Partner is a leading hedge fund manager, overseeing assets for institutional investors worldwide. With over 20 years of experience, we integrate AI and quantitative strategies to enhance investment decision-making. Our firm fosters innovation in AI/ML and financial technology, incubating and spinning out ventures. Operating across key global financial hubs, we deploy advanced AI-driven solutions for trading and investment.

With over 1,000 personnel, including 300 investment professionals, our global presence enables us to source top talent and opportunities. Brevan Howard has won industry awards for excellence in risk management, operations, and performance.

Our main hubs include London, Jersey, Geneva, New York, Austin, Hong Kong, Singapore, and Abu Dhabi.

As a Senior AI Engineer, you will be at the forefront of Generative AI and LLM-driven innovation, leading the design, development, and deployment of next-generation AI solutions for capital markets and investment applications. Your work will span fine-tuning LLMs, retrieval-augmented generation (RAG), multimodal AI, and reinforcement learning, integrating these technologies into high-impact financial systems.

We are looking for a practical AI leader who can experiment rapidly, optimize AI pipelines, and build scalable, production-grade AI/LLM solutions. You will also guide junior engineers, collaborate with investment and technology teams, and ensure that AI initiatives align with real-world financial and operational needs.

ROLES AND RESPONSIBILITIES

• Architect, fine-tune, and deploy large-scale LLMs and GenAI models, optimizing them for investment, trading, and financial analytics use cases.
• Leverage advanced techniques such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and multimodal AI to enhance LLM effectiveness in financial decision-making.
• Integrate AI/ML solutions into real-world workflows, ensuring seamless deployment and reliability in high-stakes trading and investment scenarios.
• Optimize AI inference pipelines for speed, efficiency, and scalability in production environments.
• Evaluate and experiment with the latest AI research (e.g., OpenAI, Anthropic, DeepMind, Hugging Face, open-source LLMs) to assess their applicability to finance and trading.
• Develop robust data pipelines for training and inference, ensuring high-quality and unstructured data feeds for AI models.
• Collaborate with financial, quantitative, and engineering teams to align AI solutions with business objectives.
• Contribute to the AI strategy roadmap, identifying key opportunities for GenAI adoption across the firm.
• Ensure security, compliance, and risk considerations are embedded in all AI/ML deployments.

KEY REQUIREMENTS

• 6+ years of experience in AI/ML, with a strong focus on Generative AI, LLMs, or deep learning.
• 5+ years of experience in training, fine-tuning, and deploying LLMs (e.g., GPT, LLaMA, Mistral, Falcon).
• 3+ years of experience building production-grade AI applications and integrating models into cloud-based or on-prem systems.
• 3+ years of experience in NLP, deep learning, reinforcement learning, and/or multimodal AI.
• 3+ years of experience with cloud platforms (AWS, GCP, or Azure) for deploying scalable AI/ML solutions.
• Solid experience in Python and AI/ML frameworks such as LangChain, Hugging Face, PyTorch, and TensorFlow.
• Understanding of prompt engineering, zero-shot learning, and LLM inference optimization.
• Strong software engineering skills, including containerization, CI/CD, and DevOps for AI, are mandatory. 
• Ability to lead projects and mentor junior engineers while working cross-functionally with researchers and business teams.
• Prior experience with reinforcement learning (e.g., RLHF) and autonomous agent-based AI systems is desirable.
• Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) pipelines is desirable. 
• Hands-on experience with big data processing frameworks (e.g., Spark, Ray, Dask) for large-scale AI workloads is desirable.
• Contributions to open-source AI projects, publications, or AI research are desirable.
• Excellent problem-solving skills and attention to detail in optimizing AI pipelines.
• Effective time management to balance research, experimentation, and deployment.
• Good understanding of AI governance, security, and compliance considerations.
• Prior experience working in hedge funds, investment banks, or financial markets is desirable.
• A Master's or PhD in AI, Machine Learning, NLP, or a related field is desirable.