Consultant - AI Architect

Position Overview

We are seeking an experienced AI Architect to lead the discovery phase for a complex AI

agent platform serving a lot of users in the financial services sector. This role requires deep

technical expertise in modern AI frameworks, strategic thinking for long-term architecture

planning, and the ability to work directly with demanding clients to translate business

requirements into actionable technical roadmaps.

Key Responsibilities

Discovery Phase Leadership

• Client Engagement

  : Work directly with the client to understand and document all use cases (that are required to be built) spanning semantic search, document processing, predictive modeling, and agentic analytics

• Requirements Analysis

  : Translate complex business needs into detailed technical specifications with accuracy requirements (including 100% accuracy for financial compliance use cases)

• Architecture Strategy

  : Design future-proof, modular architecture that prevents vendor lock-in while maximizing strategic flexibility

Technical Architecture Design

• Hybrid AI Stack

  : Design and validate integration of DSPy + LangGraph + PromptFlow + Azure AI services

• Scalability Planning

  : Architect solutions for 100K user base with cost-effective licensing models

• Integration Strategy

  : Plan seamless integration with existing product ecosystem

• Technology Evaluation:

  Conduct comparative analysis of AI frameworks, providing evidence-based recommendations

Deliverable Creation

• Technical Feasibility Studies

  : Comprehensive analysis for all the use-cases of the requirement

• Prototype Development

  : Build working demos demonstrating key capabilities and optimization approaches

• Cost-Benefit Analysis

  : Justify investment into a tech stacks by comparing it against other stacks for the long-term roadmap.

• Implementation Roadmap

  : Detailed phased approach from pilot to full production deployment

Strategic Planning

• Long-term Vision:

  Create long term technology evolution plan preventing costly refactoring

• Risk Assessment

  : Identify and mitigate stack lock-in risks and technical dependencies

• Go-to-Market Strategy

  : Define pilot features for rapid market entry while building toward comprehensive platform

Required Technical Expertise

AI/ML Frameworks

• DSPy

  : Deep understanding of automated prompt optimization, few-shot learning, and algorithmic tuning

• LangGraph

  : Experience with multi-agent orchestration and complex workflow design

• Azure AI & PromptFlow

  : Proficiency in Microsoft's AI services and visual workflow tools

• RAG Architectures

  : Advanced knowledge of retrieval-augmented generation systems

Cloud & Infrastructure

• Azure Ecosystem

  : Comprehensive understanding of AI Foundry, Cognitive Services, and enterprise scaling

• Microservices Architecture

  : Design of modular, swappable components

• API Design

  : RESTful services and integration patterns

• Performance Optimization

  : Large-scale system optimization and monitoring

Financial Services Domain [Good to have]

• Regulatory Compliance

  : Understanding of financial data accuracy requirements and audit trails

• Document Processing

  : Experience with legal document parsing (LPAs, fund documents)

• Predictive Analytics

  : Investment modeling and risk assessment systems

• CRM Integration

  : Customer relationship management and sentiment analysis

Required Experience

Professional Background

• 8+ years in AI/ML architecture roles with enterprise clients
• Hands-on experience with modern AI frameworks (DSPy, LangGraph, or similar)
• Proven track record of leading discovery and implementation for complex AI implementations

Client Management

• Executive Communication

  : Ability to present technical concepts to C-level stakeholders

• Requirements Gathering

  : Expert in translating business needs to technical specifications

• Stakeholder Management

  : Experience managing demanding, detail-oriented clients

• Documentation

  : Exceptional technical writing and presentation skills

Technical Leadership

• Architecture Design

  : Led design of scalable AI systems serving 50K+ users

• Technology Evaluation

  : Experience conducting comparative analysis of AI platforms

• Prototype Development:

  Hands-on coding ability for proof-of-concept development

• Cost Estimation

  : Accurate project scoping and resource planning

Preferred Qualifications

Advanced Expertise

• PhD/MS

  in Computer Science, AI/ML, or related field

• Publications

  /

  Patents

  in AI optimization or enterprise AI architecture

• Speaking Experience

  at AI conferences or industry events

• Open Source Contributions

  to AI frameworks or libraries

Industry Experience [Good to have]

• Private Equity/Investment Management

  domain knowledge

• Regulatory Technology

  experience with audit and compliance systems

• Enterprise AI Deployments

  at scale (100K+ users)

• Cost Optimization

  experience with AI workloads and licensing models

Key Success Metrics

Discovery Phase Outcomes

• Client Approval

  : Scott approves progression to development phase based on discovery results

• Technical Validation

  : All use cases of the requirement deemed technically feasible with proposed architecture

• Cost Justification

  : Clear ROI demonstration for 4x cost premium over SFDC alternative

• Timeline Adherence

  : Discovery completed within agreed timeframe and budget

Architecture Quality

• Future-Proof Design

  : Architecture prevents vendor lock-in and supports long-term evolution

• Scalability Validation

  : 100K user performance and cost models validated

• Integration Feasibility

  : Seamless integration strategy with the product confirmed

• Accuracy Framework

  : 100% accuracy requirements for financial compliance addressed

Application Requirements

Portfolio Submission

• Architecture Samples

  : 2-3 examples of complex AI system designs you've led

• Case Studies:

  Detailed examples of discovery phase leadership with measurable outcomes

• Technical Writing

  : Samples of technical documentation for executive audiences

• Client References

  : References from previous discovery/consulting engagements

Technical Assessment

• Architecture Design

  : Live design session for a sample use case from Scott's requirements

• Framework Knowledge

  : Deep-dive technical discussion on DSPy optimization approaches

• Business Acumen

  : Case study analysis of technology investment decisions

• Client Interaction

  : Mock discovery session with simulated challenging client requirements