AI Architect :

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 :

- 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 :

- 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