AI Solutions Lead

Multi-Strategy / Multi-Manager hedge fund is seeking an AI Solutions Lead to design and deliver AI-enabled solutions that improve middle and back-office workflows (for example, trade reconciliation, reporting, and compliance checks). This person will lead the evaluation, piloting, and rollout of AI capabilities that drive operational impact with work that will directly impact operational efficiency and control, without direct involvement in investment processes. Responsibilities Own AI solution delivery end-to-end: discovery, requirements, tool selection, proof-of-value pilots, implementation, rollout, and ongoing support. Design middle and back-office workflows using AI techniques to facilitate automation and efficiency. Evaluate and recommend AI tools and platforms aligned with firm priorities, including build vs. buy decisions, and lead their secure integration into existing systems. Design and build practical AI capabilities where appropriate, such as enterprise search/Q&A over internal knowledge and workflow agents grounded in firm data and controls. Engineer data and application integrations with Snowflake, Databricks, and APIs/services to deliver reliable, auditable solutions. Contribute to AI evaluation frameworks: Partner with cross-functional teams to help shape success metrics, assess test quality, monitor performance and cost, track changes over time, incorporate user feedback, maintain clear version history, and support low-risk rollout patterns. Partner with infrastructure and development engineers to ensure solutions built meet standards for scalability, security, observability, and cost/performance. Support adoption and change management for AI rollouts: Collaborate with stakeholders on readiness assessments, communications, and user enablement to help ensure smooth transitions. Champion AI governance and responsible-use practices alongside Cybersecurity: Partner to help uphold data stewardship, vendor/tool risk review, access controls, privacy/PII handling, auditability, and policy-aligned usage appropriate to the current environment. Track value realization post-deployment, including usage, time saved, error reduction, and control improvements, and continuously prioritize improvements based on impact. Communicate clearly with stakeholders: translate business needs into a technical approach, set expectations, and explain tradeoffs to non-technical audiences. Stay current on AI advancements and strategically apply them to improve operations. Own AI development tooling and platform operations: evaluate and pilot tools such as GitHub, Copilot, and Cursor, stay abreast of current trends, establish usage standards and best practices, and manage access, usage reporting, and billing/cost controls. Enable developers to use LLMs effectively for coding tasks well-suited for AI assistance: training, prompt patterns, and practical guidance. Required Qualifications Bachelor’s or higher in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience. 6-8 years in software engineering, data engineering, and/or workflow automation, plus 1–3+ years delivering AI solutions using LLMs into production. Financial services / hedge fund operations familiarity in middle and back-office Strong Python engineering skills, including data processing and service/integration development. Experience with SQL, Snowflake, Databricks, and data warehousing, including pipeline development, job orchestration, and production operations. Production deployment experience in partnership with infrastructure and development teams, including security, logging/monitoring, and CI/CD. Excellent communication and stakeholder management skills; able to explain complex concepts simply and drive alignment. Strong problem-solving skills and the ability to work independently and collaboratively. Preferred Qualifications Familiarity with emerging agentic AI patterns, including multi-agent systems, tool integration, and long-running workflows, with the ability to translate these into scalable enterprise solutions. Experience with governance and risk controls for AI, including privacy, PII handling, vendor risk, and auditability. Experience building LLM-enabled applications, such as knowledge search/Q&A with retrieval, and setting up evaluation/testing to measure quality, reliability, and safety. #J-18808-Ljbffr