Head of AI Architecture

Vista Equity Partners Management, LLC; London; On-site; GBP 80,000 - 120,000; Full time

Job description

We are Allvue Systems, the leading provider of software solutions for the Private Capital and Credit markets. Whether a client wants an end-to-end technology suite, or independently focused modules, Allvue helps eliminate the boundaries between systems, information, and people. We’re looking for ambitious, smart, and creative individuals to join our team and help our clients achieve their goals. Working at Allvue Systems means working with pioneers in the fintech industry. Our efforts are powered by innovative thinking and a desire to build adaptable financial software solutions that help our clients achieve even more. With our common goals of growth and innovation, whether you’re collaborating on a cutting-edge project or connecting over shared interests at an office happy hour, the passion is contagious. We want all of our team members to be open, accessible, curious and always learning. As a team, we take initiative, own outcomes, and have passion for what we do. With these pillars at the center of what we do, we strive for continuous improvement, excellent partnership and exceptional results. Come be a part of the team that’s revolutionizing the alternative investment industry. Define your own future with Allvue Systems!

Responsibilities

• Define and own the end-to-end AI strategy, including model selection, data pipelines, and infrastructure.
• Lead the development of the next-gen AI platform for Allvue Systems, working closely with a team of engineers and developers.
• Design innovative and cutting-edge AI solutions that will transform the alternative investment industry.
• Stay up-to-date with the latest advancements and trends in AI technology to ensure the platform remains at the forefront of the industry.
• Provide technical expertise and guidance to the team, ensuring the successful implementation and integration of AI solutions.
• Collaborate with various departments and stakeholders to identify and prioritize AI needs and requirements.
• Conduct research and experiments to continuously improve and enhance the AI platform.
• Collaborate with external partners and vendors to leverage their expertise and capabilities in AI.
• Communicate effectively with stakeholders at all levels to provide updates, gather feedback, and present project proposals.

Qualifications

• At least ten years experience designing and architecting large-scale, distributed systems.
• At least three years designing and deploying AI/ML/NLP/Deep Learning solutions.
• Demonstrable experience as an expert in AI, with a consistent record of leading the architecture of multiple LLM and Generative AI solutions.
• Expertise in LLM ecosystems, including platforms like Azure OpenAI, Google Vertex, and AWS Bedrock, and mastery of relevant tech stacks.
• Proficient in cloud platforms (AWS, Azure, GCP) with experience in deploying and leading complex AI/ML workloads.
• Implemented production applications using Retrieval-augmented generation (RAG) concepts and managed large Knowledge Bases (KBs) with embeddings, chunking and other optimization techniques within VectorDBs.
• Expert programming skills in languages such as Python, Java, or C++, and working knowledge of deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face.
• Understanding of generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models (e.g., GPT, BERT for text).
• Experience in collaborating with data engineers, implementing advanced AI, ML, and NLP techniques using industry standard frameworks.
• Deep familiarity with vector databases and data processing techniques for LLMs, including model chunking.
• Hands-on experience with LLM application frameworks, such as Langchain.
• Proven expertise in CI/CD pipeline management within the Generative AI space, including LLMOps.
• Comprehensive understanding of machine learning and NLP algorithms, with hands-on experience in frameworks like Scikit-Learn, TensorFlow, and PyTorch.
• Strong knowledge of generative AI techniques, including deep generative models, auto regressive models, and reinforcement learning for generative tasks.