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BRG

Global consulting transforming data into insights

Senior Managing Consultant/Managing Consultant AI Architect - Financial Services Operations & Technology

AI Application EngineeringConfirmed live in the last 24 hours
Full-time$158k - $210kIn-person

About the Job

Requirements

  • A minimum of 5 years of prior applicable work experience is required for the Managing Consultant level; a minimum of 7 years is required for the Senior Managing Consultant level.
  • Demonstrate senior, hands-on delivery of AI applications from architecture through production implementation; equivalent practical experience is recognized regardless of formal education or certification.
  • Have strong Python engineering skills and the ability to build tested services, model integrations, tool interfaces, and APIs that remain maintainable beyond an initial prototype.
  • Have implemented retrieval-augmented generation (RAG) and agentic workflows and be able to explain trade-offs in retrieval, orchestration, state management, tool permissions, and human oversight without depending on one vendor's stack.
  • Be able to design evaluations using representative tasks and expert-reviewed ground truth, and use results to improve accuracy, reliability, safety, latency, and cost.
  • Have applied security and governance controls to AI systems, including defenses against prompt injection, access-aware retrieval, sensitive-data exposure, and unauthorized tool execution.
  • Have deployed and operated AI services using cloud infrastructure, containers or equivalent deployment patterns, continuous integration and continuous delivery, versioning, monitoring, and rollback practices.
  • Be able to lead a complex technical workstream, mentor engineers, work directly with client business, technology, and risk teams to communicate architectural trade-offs, and contribute credible delivery estimates.
  • Be able to travel to client sites as needed.

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 application programming interfaces with client data and operational systems.
  • Design and build retrieval-augmented generation 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 large language model operations 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.

Desired Qualifications

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

About the Company

BRG

Global consulting transforming data into insights

ThinkBRG provides professional advisory and consulting services worldwide, focusing on turning raw data into actionable insights for clients across many industries. Its approach combines data analysis with expert consulting to help organizations make better decisions. The process relies on a collaborative, globally distributed team that shares knowledge daily to produce high-quality insights tailored to each project’s complexity, expertise needs, and duration. ThinkBRG differentiates itself through deep industry understanding, a proven track record, and a global footprint that enables serving a diverse client base from small businesses to large corporations. The company’s goal is to help clients transform information into practical recommendations and actions that drive decision-making and measurable outcomes, generating revenue through consulting engagements aligned with project scope and duration.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$84.5M
Headquarters
Emeryville, CA
Founded
2010

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