Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case.
About the PracticeBerkeley Research Group is launching a new Financial Institution Operations & Technology practice. We combine deep financial services expertise with AI-enabled delivery to improve operations across commercial and consumer lending, payments, and asset and wealth management.
Help build a new practice within an established consulting firm. You will develop market-leading AI solutions, work with our AI Co-lab to frame solutions with vendor partners, collaborate with client teams, and build reusable digital assets. You will also contribute to recruiting, new offerings, thought leadership, and delivery improvement.
The RoleWe are considering candidates at the Managing Consultant and Senior Managing Consultant levels. The final title and compensation will reflect the selected candidate's relevant experience and the scope of responsibility. You will combine solution architecture with hands-on engineering to turn financial services operating problems into production-ready AI solutions. Working closely with client teams, you will translate domain expertise into governed workflows with clear boundaries for automation and human judgment. You will guide engineers and collaborate with domain experts, data and application engineers, and change and risk practitioners to deliver reusable solutions and practical implementation roadmaps.
What You'll Do- 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 in the AI Co-lab with vendor partners and domain experts 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.
QualificationsRequired- A minimum of 5 years of prior applicable work experience is required to be considered for the Managing Consultant level. To be considered at the Sr. Managing Consultant level, a minimum of 7 years of experience is required.
- You can demonstrate senior, hands-on delivery of AI applications from architecture through production implementation, with equivalent practical experience recognized regardless of formal education or certification.
- You have strong Python engineering skills and can build tested services, model integrations, tool interfaces, and APIs that remain maintainable beyond an initial prototype.
- You have implemented RAG and agentic workflows and can explain trade-offs in retrieval, orchestration, state management, tool permissions, and human oversight without depending on one vendor's stack.
- You can design evaluations using representative tasks and expert-reviewed ground truth, and use the results to improve accuracy, reliability, safety, latency, and cost.
- You have applied security and governance controls to AI systems, including defenses against prompt injection, access-aware retrieval, sensitive-data exposure, and unauthorized tool execution.
- You have deployed and operated AI services using cloud infrastructure, containers or equivalent deployment patterns, CI/CD, versioning, monitoring, and rollback practices.
- You can lead a complex technical workstream, mentor engineers, and work directly with client business, technology, and risk teams to communicate architectural trade-offs and contribute credible delivery estimates.
- Ability to travel to client sites as needed.
Preferred- 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 that compare 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.
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.
Managing Consultant salary range: $158,000-180,000
Senior Managing Consultant salary range: $190,000 - $210,000
Job title and level will be determined based on qualifications and experience.
Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.
