Platform Engineer Architecture

Job Description

Company Description Acuity Knowledge Partners (Acuity) is a global leader in providing tailored research, analytics, and technology solutions to the financial services industry, including asset managers, investment banks, private equity firms, and hedge funds. Headquartered in London, Acuity serves over 500 financial institutions worldwide, with a team of more than 6,000 experts across 10 global locations. Established as an independent business in 2019, following its separation from Moody?s Corporation, Acuity continues to grow under the investment of Permira, with Equistone as a minority shareholder. The organization is committed to empowering financial institutions to achieve operational excellence and revenue growth by leveraging its unique expertise and innovative technology. Job Purpose We are seeking a seasoned Platform Architect to spearhead the design and development of a cutting-edge, Gen AI-enabled workflow automation platform tailored for the financial services sector. This role requires a visionary leader with deep expertise in data architecture, vector databases, generative AI (Gen AI), large language models (LLMs), and agent-based frameworks. The ideal candidate will possess knowledge of low-code solutions while ensuring the creation of scalable, reusable components that drive business value in a highly regulated domain. Required Qualifications & Skills Education Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field Experience 12+ years in software architecture, platform engineering, or related areas Proven track record designing complex, scalable systems within financial services or similarly regulated environments Technical Expertise Data Architecture: In-depth knowledge of relational, NoSQL, and vector databases Python Frameworks: In-depth knowledge of Python-based frameworks for data analysis and distributed systems Generative AI & LLM: Hands-on experience with generative AI and large language models (LLMs) to design intelligent solutions for financial services use cases Machine Learning Frameworks: Good to have knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) Microservices & Containers: Solid understanding of microservices architecture and container orchestration (Docker, Kubernetes) DevOps Practices: Strong background in DevOps practices and tools (CI/CD, Jenkins, GitOps, etc.) Research Mindset & Innovation: Demonstrated ability to adopt a research-oriented approach to incubate, experiment with, and validate emerging technologies and solutions that address complex business challenges Behavioral Competencies Exceptional communication skills to translate complex technical concepts into actionable business solutions Leadership and mentoring capabilities to nurture cross-functional teams Proven track record in collaborating with stakeholders and influencing technology decisions at the executive level Ability to conduct Proof of Concepts (POCs) and drive adoption across teams Strong capability to handhold and guide teams through LLM-based solution implementations Key Responsibilities 1. Platform Architecture & Design Develop a scalable, modular, and extensible system architecture to support various financial services products Ensure compliance with industry regulations, security standards, and data privacy requirements 2. Data Architecture & Integration Define common data models and best practices for integrating vector databases and other modern data storage solutions Implement data pipeline solutions in AWS/Azure environments 3. Generative AI & LLM Solutions Guide the integration of large language models (LLMs) into platform capabilities to enhance automation, decision-making, and user experience Leverage LLM technology to address financial services use cases such as investment research, market analysis, and data synthesis Conduct proof of concepts (POCs) to validate LLM-based solutions and demonstrate business value Handhold and mentor teams through the implementation lifecycle, ensuring successful adoption of LLM technologies Collaborate with domain experts and data scientists to design intelligent solutions that maximize the effectiveness of LLM-based approaches 4. Low-Code and RPA Knowledge (Good to Have) Possess functional knowledge of low-code framework operations Establish best practices for building reusable components and connectors that can be leveraged by various teams Provide architectural guidance and training to development teams to maximize the impact of low-code platforms 5. Scalability & Reusability Design and advocate for microservices or serverless architectures, ensuring high availability, reliability, and performance Develop reusable components, services, and frameworks to standardize development across the organization Implement CI/CD pipelines, automated testing, and DevOps practices to streamline deployment and maintenance 6. Research & Innovation Incubation Adopt a research-oriented mindset to explore and experiment with emerging technologies and methodologies Incubate and validate new solutions through structured experimentation and proof of concept initiatives Foster a culture of continuous learning and innovation, encouraging teams to explore novel approaches to solving business challenges Document learnings and best practices from experimental initiatives to drive organizational knowledge 7. Stakeholder Collaboration & Leadership Work closely with cross-functional teams, including product managers, security experts, and business stakeholders, to align technology solutions with strategic objectives Present architectural roadmaps, proofs of concept, and technical recommendations to executive leadership Mentor and guide junior architects and developers, fostering a culture of innovation, excellence, and collaboration Lead POC initiatives and provide hands-on support to ensure successful implementation and knowledge transfer