Independent & Open-Source AI Platform Engineering

• Outlined a SageMaker-based MLOps architecture to support model training, versioning, and controlled inference lifecycles for SDA workflows
• Conceptualized pipeline patterns for experimentation, validation, and promotion, aligned with existing FastAPI-based inference services
• Identified cost-aware design principles, including ephemeral training jobs and shortlived endpoints, to guide future implementation
• Architecture concept finalized; implementation planned to start in Jan 2026

• Independent & Open-Source AI Platform Engineering 
• A hybrid graph + vector AI platform providing natural-language analytics on startups, investors, and market relationships
  • Architected Neo4j + Weaviate + LangChain based AI knowledge system.
  • Built NL ? Cypher ? Graph Query Engine enabling multi-hop relationship insights.
  • Implemented Vector RAG search for ranked investor/startup intelligence.
  • Designed multi-agent orchestration with Prometheus-based observability.
  • Delivered microservices, Traefik routing, Redis caching, and React AI dashboards.
  • Built Semantic Search (Vector RAG) using Weaviate + OpenAI embeddings, returning ranked startup/investor insights.
  • Developed Hybrid AI pipeline merging graph results + vector context + GPT-4o synthesis for multi-hop queries

Independent & Open-Source AI Platform Engineering 

• Outlined a phased MLOps architecture to extend the existing Dealflow platform using GCP Vertex AI
• Conceptualized Vertex AI pipeline patterns for feature extraction, embedding generation, batch inference, and scheduled retraining workflows
• Planned integration approach between Cloud Run?based inference services and managed vector search options
• Identified on-demand, cost-controlled execution patterns to avoid always-on ML endpoints
• Architecture concept finalized; pilot implementation planned to start in Jan 2026

Independent & Open-Source AI Platform Engineering 

• Designing and implementing an enterprise-grade LLM and Retrieval-Augmented Generation (RAG) platform aligned with NVIDIA AI platform best practices, with focus on scalability, modularity, and production readiness
  • Developing end-to-end AI workflows covering data ingestion, document chunking, embedding generation, vector indexing, retrieval, and LLM response synthesis
  • Implementing agent-based orchestration patterns (retriever, reasoning, synthesis agents) using LangChain/LangGraph concepts to support extensible, multi-step reasoning pipelines
  • Building scalable inference architecture with clear separation between control plane (API orchestration) and inference plane (model execution), designed for GPUaccelerated environments
  • Applying cloud-native AI design principles including stateless services, horizontal scaling, and on-demand execution to enable cost-controlled AI workloads
  • Integrating observability-first design, defining and instrumenting metrics for retrieval latency, inference performance, agent execution paths, and system health
  • Producing detailed technical design artifacts (architecture diagrams, request?response flows, deployment models) alongside evolving README and implementation documentation
  • Designing production deployment strategies covering containerization, service boundaries, monitoring hooks, and extensibility within the NVIDIA AI ecosystem
  • Addressing enterprise-grade requirements such as reliability, traceability, explainability, and long-term maintainability throughout the implementation lifecycle

• Designed and implemented a scalable, version-controlled data workflow and mapping platform for enterprise users
• Architected frontend applications using React, TypeScript, and modern UI frameworks to support complex multi-step workflows
• Refactored core business logic from frontend to backend using FastAPI and SQLAlchemy to improve performance and maintainability
• Implemented secure REST APIs with JWT authentication, schema validation, and robust error handling
• Integrated AI-assisted workflows (RAG-based prototypes) to enhance data search, transformation, and usability
• Collaborated closely with product, finance, and engineering teams and mentored junior developers