3 Promptflow Jobs

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

Responsibilities: * Collaborate with dev team on API testing using GIT and CI/CD pipeline. * Develop automated tests with Python, PyTest, and frameworks.

Role & responsibilities We are seeking a highly skilled QA Automation Engineer with 3-5 years of experience in software development, SDET, or QA automation, with a strong focus on backend systems, APIs, or complex data pipelines. This role demands deep expertise in Python programming and a strong understanding of automation tools, CI/CD processes, and API testingparticularly within evolving AI-driven environments. Design, implement, and maintain robust automation frameworks using Python (Pytest or similar). Conduct thorough testing of backend systems , data pipelines, and RESTful APIs . Build test cases and scripts to support automated validation of AI-integrated services. Use tools like Postman or the requests library for API testing. Manage source control with Git and integrate tests into CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions). Analyze and document defects with clarity, and collaborate with developers for resolution. Continuously adapt and learn new testing approaches for modern AI/ML-powered systems . Must-Have Skills 35 years of experience in software development, QA automation, or SDET roles. Strong hands-on programming skills in Python . Proven experience with automation frameworks like Pytest . Solid experience in REST API testing and tools like Postman or requests. Familiarity with CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions. Excellent communication skills with the ability to articulate technical concepts clearly. Fast learner with the ability to adapt quickly to evolving AI/ML technologies . Preferred Qualifications Experience with cloud platforms (AWS, GCP, Azure), especially AI/ML services. Exposure to testing AI-based features or systems, especially in the telecom domain . Knowledge of UI automation tools like Selenium or Playwright (nice to have) Familiarity with structured testing of AI service pipelines and performance benchmarking. What We Offer Remote work flexibility Competitive compensation based on skill and experience Opportunity to work on cutting-edge AI-powered platforms A collaborative, learning-first culture with global project exposure