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

Berkeley Research Group; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Berkeley Research Group is launching a new Financial Institution Operations & Technology practice. You will design production AI services, translate banking operations into governed workflows, and deliver reusable assets and roadmaps.

The role targets Managing or Senior Managing Consultants with strong Python delivery, RAG workflows, model integrations, and CI/CD practices. Travel to client sites is required. Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston.

Qualifications

• Minimum 5 years of prior applicable work experience for Managing Consultant level; 7+ years for Sr. Managing Consultant.
• Senior, hands-on delivery of AI applications from architecture to production implementation.
• Strong Python engineering skills with tested services, model integrations, tool interfaces, and APIs.
• Experience with RAG and agentic workflows; ability to discuss trade-offs in retrieval, orchestration, state management, tools, and oversight.
• Security and governance controls for AI systems, including defenses against prompt injection and data exposure.
• Experience deploying AI services in cloud, containers, CI/CD, versioning, monitoring, and rollback.

Responsibilities

• Lead AI architecture and engineering workstreams; design solution plans with clear automation boundaries.
• Implement production AI services in Python; integrate models, retrieval, rules, tools, and APIs with client data.
• Design and test RAG and agentic workflows with source grounding and escalation paths.
• Collaborate with vendor partners and domain experts to frame solutions and compare capabilities.
• Define evaluation datasets and acceptance criteria; test retrieval quality, task accuracy, and safety.
• Apply MLOps practices for versioning, testing, deployment, and observability.
• Mentor engineers and lead technical reviews; validate adoption and benefits.
• Capture expert decision patterns for reusable workflows and future pursuits.

Skills

Python engineering

AI architecture

Hands-on delivery

RAG workflows

Model integrations

CI/CD

Security governance

Mentoring engineers

Job description

We Do Consulting Differently

Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case.

About the Practice

Berkeley 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 Role

We 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.

Qualifications

Required

• 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-ba