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
