The Role

As a Senior AI Solution Engineer, you partner with business stakeholders to transform ambiguous challenges into practical AI solutions that enhance investment processes and drive business outcomes. You serve as the critical bridge between business teams who own the problem and the AI platform that delivers the solution. You lead discovery sessions, define the solution approach, develop the code, and take ownership of deployment and production support. This is a hands-on engineering role that requires equal comfort collaborating with business teams in working sessions and building technical solutions such as retrieval pipelines, AI agents, and scalable AI-powered applications. You will play a key role in establishing best practices and setting the standard for how generative AI solutions are designed, deployed, governed, and operated responsibly at enterprise scale across the organization.

Your responsibilities:

• Partner directly with business stakeholders to understand workflows, identify high-value opportunities, and translate ambiguous business needs into clear technical requirements
• Design, build, and operate production-grade generative AI applications, including copilots, assistants, knowledge-search platforms, and agentic workflow solutions, ensuring reliability and scalability beyond proof-of-concept implementations.
• Architect and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines, including document parsing, data ingestion, chunking strategies, embeddings, vector databases, retrieval mechanisms, and prompt management.
• Develop AI agents and agentic workflows capable of planning and executing complex multi-step tasks within defined, auditable boundaries, supported by robust guardrails for safe and predictable behavior.
• Practice evaluation-driven development by defining acceptance criteria upfront, building evaluation frameworks, and measuring correctness, latency, and hallucination rates to ensure solution quality and prevent production regressions.
• Own the complete solution lifecycle, from discovery and design through development, deployment, and operational excellence, including observability, cost monitoring, and audit capabilities to proactively identify performance degradation.
• Apply FinOps and cost-optimization practices to AI workloads by tracking and managing token usage, inference costs, and infrastructure spend to ensure solutions remain cost-effective as they scale.
• Apply responsible AI principles and risk-based judgment appropriate to each use case, collaborating with risk and compliance teams to establish controls, human oversight mechanisms, and audit trails that enable rapid and safe adoption of AI solutions.
• Embed security, privacy, and compliance controls into solution architectures, including Identity and Access Management (IAM), encryption, and audit logging.
• Partner with Information Security (InfoSec) and Data Governance teams to ensure compliance with regulatory requirements and internal policies, including SOC 2 and applicable data privacy regulations.
• Develop reusable tools, frameworks, patterns, and playbooks, and share insights with platform, product, and engineering teams to accelerate organizational learning and execution.
• Produce clear technical documentation, operational runbooks, and architectural diagrams that enable teams to understand, maintain, operate, and extend the solutions being built.
• Demonstrate full-stack engineering capabilities across development environments, supporting end-to-end solution delivery from front-end interfaces to back-end services and infrastructure.