Job Title

Principal AI Architect - Conversational AI & RAG SystemsDesignation -Member of Technical StaffLocation: BangaloreExperience: 8+ yearsMode of work: Work from OfficeRole OverviewWe're looking for a Principal AI Architect to lead the design and architecture of our conversational AI platform that powers private credit analysis for institutional investors. You'll architect end-to-end RAG pipelines, build MCP servers over complex financial datasets, and design agentic workflows that enable multi-step reasoning across deals, funds, and market data.This is a hands-on architectural role where you'll both design systems and guide implementation. You'll lead our Alpha AI squad, partner with R&D leadership on research direction, and set standards for AI/ML engineering across the company.Impact: Your architecture will directly determine how fast and accurately our AI can analyse private credit portfolios ($100M+ AUM clients), which translates to deal-closing speed for asset managers.What You'll Build1. Conversational AI ArchitectureDesign MCP (Model Context Protocol) server architecture for structured financial data accessArchitect RAG pipeline: retrieval → re-ranking → context funneling → generationBuild tool orchestration layer: function calling, parameter validation, response parsingDesign agentic workflows with task decomposition and multi-turn reasoningExample: Architect a system where /"Compare portfolio risk across Q1 and Q2/" triggers: data extraction → metric computation → trend analysis → response synthesis2. Retrieval System DesignDesign hybrid search strategy combining keyword (BM25) + semantic (vector) searchArchitect vector database strategy: embedding models, indexing (HNSW/IVF), shardingImplement re-ranking with cross-encoders and Maximum Marginal Relevance (MMR)Build citation system linking every generated statement to source document + page numberOptimize query expansion, passage relevance scoring, and metadata filteringPerformance target: Retrieval recall@10 > 90%, answer citation accuracy > 95%3. MCP Server DevelopmentBuild MCP servers over structured datasets: deal terms, fund performance, financials, market compsDesign tool definitions with strict schemas: function signatures, parameter types, validation rulesImplement guardrails: confidence thresholds, schema validation, human-in-the-loop triggersExample: MCP server for /"Get deal covenants/" must return structured JSON with covenant type, threshold, measurement period, breach conditions4. System Observability & QualityBuild observability framework: log queries, retrieval candidates, tool calls, responses, latenciesDesign RAG quality metrics: answer accuracy, citation precision, retrieval recall, hallucination rateImplement A/B testing framework for prompt strategies and retrieval configurationsCreate debugging dashboards for query→retrieval→generation pipeline tracingTarget: 98%5. Technical LeadershipLead Alpha AI squad (4-6 engineers): guide architectural decisions, review designs, unblock technical challengesConduct design reviews for all AI/ML features across Analyst Platform and Insights EngineDefine AI/ML platform standards: model serving, versioning, monitoring, rollback proceduresMentor full-stack engineers on LLM integration patterns, prompt engineering, and AI best practicesPartner with Head of R&D on research direction: what models to fine-tune, what benchmarks to chaseMust-Have QualificationsExperience & Expertise:8+ years in AI/ML engineering with 3+ years designing production RAG or conversational AI systemsProven track record architecting and deploying LLM-powered applications serving >10K users or $10M+ revenueDeep expertise in retrieval systems: vector databases (Pinecone/Weaviate/Qdrant), embedding models, hybrid search, re-rankingStrong understanding of LLM architectures, fine-tuning, and prompt engineering (few-shot, CoT, ReAct patterns)Experience building agentic workflows with tool use, function calling, and multi-step reasoningTechnical Skills:Vector databases: Production experience with Vespa, Pinecone etcLLM frameworks: LangChain, LlamaIndex, or custom orchestration for complex workflowsEmbedding models: SentenceTransformers, OpenAI embeddings, domain-specific fine-tuningMCP or similar protocols: Experience building structured data access layers for LLMsPython (expert level): FastAPI, asyncio, Pydantic for API design and validationEvaluation frameworks: RAGAS, LangSmith, custom benchmarking for RAG qualityLeadership:Experience leading technical teams (4+ engineers) through architecture design and implementationTrack record of setting technical standards and conducting design reviews across multiple productsAbility to translate business requirements into technical architecture and success metricsAdded AdvantageDomain & Scale:Experience in FinTech, credit analysis, or financial data platforms (understanding of deals, covenants, financials)Built systems processing >1M documents or 100GB+ knowledge basesFamiliarity with private credit, structured finance, or asset management workflowsAdvanced Techniques:Fine-tuned Small Language Models for domain-specific tasks (e.g., financial entity extraction, classification)Implemented query decomposition and planning Built multi-modal RAG systems (text + tables + charts)Experience with prompt optimization at scale (DSPy, automatic prompt tuning)Infrastructure:Designed model serving infrastructure: inference optimization, batching, caching, A/B deploymentBuilt real-time streaming pipelines for document ingestion and embedding generationExperience with Kubernetes, Docker, and ML Ops tooling (MLflow, Weights & Biases)Research:Published papers or blog posts on RAG, retrieval, or agentic AIContributed to open-source projects in LLM tooling or vector searchWho We Are-Alphastream.ai envisions a dynamic future for the financial world, where innovation is propelled by state-of-the-art AI technology and enriched by a profound understanding of credit and fixed-income research. Our mission is to empower asset managers, research firms, hedge funds, banks, and investors with smarter, faster, and curated data. We provide accurate, timely information, analytics, and tools across simple to complex financial and non-financial data, enhancing decision-making. With a focus on bonds, loans,financials and sustainability, we offer near real-time data via APIs and PaaS (Platform as a Service) solutions that act as the bridge between our offerings and seamless workflow integration.To learn more about us: we offer/"At Alphastream.ai we offer a dynamic and inclusive workplace where your skills are valued and your career can flourish. Enjoy competitive compensation, a comprehensive benefits package, and opportunities for professional growth. Immerse yourself in an innovative work environment, maintain a healthy work-life balance, and contribute to a diverse and inclusive culture. Join us to work with cutting-edge technology, and be part of a team that recognizes and rewards your achievements, all while fostering a fun and engaging workplace culture./"Disclaimer-Alphastream.ai is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of all communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.