Sr. Managing Consultant/Managing Consultant AI Architect - Financial Services Operations & Technology

BRG•Boston, MA

1d•$158,000 - $210,000

About The Position

Berkeley Research Group is launching a Financial Institution Operations & Technology practice that combines financial services expertise with AI-enabled delivery to improve operations across commercial and consumer lending, payments, and asset and wealth management. The role will help build the new practice within an established consulting firm by developing AI solutions, working with the AI Co-lab and vendor partners, collaborating with client teams, and building reusable digital assets. The successful candidate will also contribute to recruiting, new offerings, thought leadership, and delivery improvement. BRG is considering candidates at the Managing Consultant and Senior Managing Consultant levels. The final title and compensation will reflect relevant experience and scope of responsibility. The position combines solution architecture with hands-on engineering to turn financial services operating problems into production-ready AI solutions. The role works with client teams to translate domain expertise into governed workflows with clear boundaries for automation and human judgment, guides engineers, and collaborates with domain experts, data and application engineers, and change and risk practitioners to deliver reusable solutions and implementation roadmaps. Success means delivering reusable AI solutions that perform reliably under expert oversight, meet client security and control requirements, and produce measurable improvements in operational cost, capacity, and service quality. Preferred locations are New York, Chicago, Washington, D.C., Dallas, and Boston; strong candidates elsewhere may be considered case by case. Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.

Requirements

At least 5 years of applicable work experience for the Managing Consultant level; at least 7 years for the Senior Managing Consultant level.
Senior, hands-on delivery experience taking AI applications from architecture through production implementation; equivalent practical experience is recognized regardless of formal education or certification.
Strong Python engineering skills, including the ability to build tested services, model integrations, tool interfaces, and APIs that remain maintainable beyond an initial prototype.
Experience implementing RAG and agentic workflows and explaining trade-offs in retrieval, orchestration, state management, tool permissions, and human oversight without relying on one vendor's stack.
Ability to design evaluations using representative tasks and expert-reviewed ground truth, and to use results to improve accuracy, reliability, safety, latency, and cost.
Experience applying security and governance controls to AI systems, including defenses against prompt injection, access-aware retrieval, sensitive-data exposure, and unauthorized tool execution.
Experience deploying and operating AI services using cloud infrastructure, containers or equivalent deployment patterns, CI/CD, versioning, monitoring, and rollback practices.
Ability to lead a complex technical workstream, mentor engineers, work directly with client business, technology, and risk teams, communicate architectural trade-offs, and contribute credible delivery estimates.
Ability to travel to client sites as needed.
Ability to verify legal authorization to work in the United States without employer sponsorship now or in the future.

Nice To Haves

Applied AI to document-heavy or rules-based financial services processes in lending, servicing, collections, payments disputes, or investment operations.
Facilitated vendor evaluations or Co-lab experiments comparing hosted and open-weight models, orchestration frameworks, and deployment options using documented criteria.
Built expert-in-the-loop systems that capture reviewer feedback and convert domain expertise into reusable decision rules, evaluation assets, or workflow components.
Experience with multimodal document understanding, structured extraction, or advanced retrieval techniques that improve performance on complex operational tasks.

Responsibilities

Lead AI architecture and engineering workstreams, translating client operating problems into solution designs, delivery plans, and explicit boundaries for automation and human judgment.
Implement production AI services in Python, integrating models, retrieval, business rules, tools, and APIs with client data and operational systems.
Design and build RAG and agentic workflows with source-grounded responses, constrained tool use, human review, exception routing, and clear escalation paths.
Work with vendor partners and domain experts in the AI Co-lab to frame solutions, prototype alternatives, and compare model and platform capabilities against client needs.
Define evaluation datasets and acceptance criteria with client experts, and test retrieval quality, task accuracy, unsafe behavior, latency, cost, and the effectiveness of human oversight.
Implement LLMOps practices for prompt and model versioning, regression testing, deployment, observability, feedback capture, rollback, and ongoing performance review.
Design and test safeguards for prompt injection, unauthorized data access, sensitive-information exposure, and unsafe tool actions in partnership with client security and risk teams.
Collaborate with data and full-stack engineers to deploy secure, maintainable solutions and equip client teams with operating procedures, technical documentation, and support arrangements.
Mentor engineers and lead technical reviews while working with domain, change, and risk colleagues to validate adoption, control effectiveness, and measurable operating benefits.
Capture expert decision patterns in reusable workflows, evaluation suites, and reference architectures, and contribute feasibility assessments and technical scope estimates to future pursuits.
Contribute to recruiting, new offerings, thought leadership, and delivery improvement.
Travel to client sites as needed.