AI Architect

Posted on July 7, 2025

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Job Description

  • 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

Required Skills

ai architect