Senior Manager of Software Engineering - Research & Development for the NEO and BRIE Platforms

Job Responsibilities

• Provides overall direction, oversight, coaching, and career development for a team of 4â5 software engineers across varying experience levels, cultivating a high-performing R&D function.
• Owns the research and development agenda for the NEO agent runtime and BRIE data platforms, translating platform strategy into a prioritized book of work spanning query federation, policy, storage, cataloging, and caching/memory.
• Led structured technology evaluations and proofs of concept - defining success criteria, benchmarking candidates, and producing clear recommendations that hold up to architectural and security review.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and consistent validation standards (secure coding, peer review, automated testing), while promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
• Reviews and debugs code and designs authored by the team, and remains hands-on enough to guide critical technical decisions.
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability within existing systems and information architecture.
• Makes decisions that influence team resources, budget, and tactical operations, and is accountable for those outcomes.
• Ensures successful collaboration across engineering teams, product, and platform stakeholders, and communicates findings and trade-offs to senior leadership.

Required Qualifications, Capabilities, And Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience, with 2+ years leading and coaching teams of technologists.
• Hands-on practical experience delivering system design, application development, testing, and operational stability.
• Hiring, developing, and recognizing talent, and setting expectations for team output and engineering practices.
• Advanced in one or more programming language(s): Java, Python, or Rust.
• Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling of inputs/outputs.
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Proficient in all aspects of the Software Development Life Cycle.
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, distributed data systems).
• In-depth knowledge of the financial services industry and their IT systems.

Preferred Qualifications, Capabilities, And Skills

• Experience evaluating and operating query federation engines (e.g., Starburst/Trino) across heterogeneous data sources.
• Experience with centralized policy and authorization engines (e.g., OPA/Rego, OpenFGA) for runtime and structural access control.
• Experience deploying and tuning columnar/OLAP stores (e.g., ClickHouse) in both on-premises and AWS environments.
• Familiarity with open-source data catalog solutions and open table formats (Apache Iceberg) for lakehouse architectures.
• Experience with caching and in-memory data solutions (e.g., Redis) for low-latency retrieval and agent/session memory.
• Exposure to LLMs, RAG architectures, vector databases, and embedding-based retrieval systems.
• Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes).