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

About the role

Berkeley Research Group is launching a new Financial Institution Operations & Technology practice, combining deep financial services expertise with AI-enabled delivery to improve operations across commercial and consumer lending, payments, and asset and wealth management. This role helps build a new practice within an established consulting firm: developing market-leading AI solutions, working with the AI Co-lab to frame solutions with vendor partners, collaborating with client teams, and building reusable digital assets. The position also contributes to recruiting, new offerings, thought leadership, and delivery improvement.

We are considering candidates at the Managing Consultant and Senior Managing Consultant levels; final title and compensation will reflect the selected candidate’s relevant experience and scope of responsibility. The role combines solution architecture with hands-on engineering to turn financial services operating problems into production-ready AI solutions, translating domain expertise into governed workflows with clear boundaries for automation and human judgment.

Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case. Ability to travel to client sites as needed. Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.

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 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.

Qualifications

Required

• Minimum 5 years of applicable work experience for Managing Consultant level; minimum 7 years for Senior Managing Consultant level.
• Demonstrated senior, hands-on delivery of AI applications from architecture through production implementation (equivalent practical experience recognized regardless of formal education or certification).
• Strong Python engineering skills: building tested services, model integrations, tool interfaces, and APIs that remain maintainable beyond an initial prototype.
• Implemented RAG and agentic workflows; can explain trade-offs in retrieval, orchestration, state management, tool permissions, and human oversight without depending on one vendor's stack.
• Can design evaluations using representative tasks and expert-reviewed ground truth, and use results to improve accuracy, reliability, safety, latency, and cost.
• Applied security and governance controls to AI systems, including defenses against prompt injection, access-aware retrieval, sensitive-data exposure, and unauthorized tool execution.
• Deployed and operated AI services using cloud infrastructure, containers or equivalent deployment patterns, CI/CD, versioning, monitoring, and rollback practices.
• 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.

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.

Pay

• 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.