Emmanuel Ifeanyi

• • Designed and built production software applications using FastAPI, LangGraph, and OpenAI APIs, exposing reusable AI services that automated insight generation, healthcare analytics, and intelligent decision support across enterprise workflows.

• • Architected multi-agent AI workflows with shared state, structured outputs, function calling, and resilient fallback strategies, enabling reliable reasoning across complex production workloads.

• • Built production Retrieval-Augmented Generation (RAG) pipelines using hybrid retrieval, semantic search, reranking, and optimized chunking strategies, reducing manual analysis time by 40% while maintaining high retrieval accuracy.

• • Led the migration of 200+ enterprise ETL models from Snowflake/dbt to Azure Databricks using incremental processing and CDC, reducing pipeline runtime by 50% while establishing a scalable AI-ready data platform.

• • Designed modern AI and data platform architecture, implementing LLMOps practices including prompt evaluation, latency monitoring, token analytics, production observability, Medallion Architecture, and Data Vault modelling to support enterprise AI workloads.

• • Designed and developed enterprise-scale data platforms using PySpark, SQL, Palantir Foundry, and Skywise, transforming heterogeneous operational data into trusted datasets supporting enterprise analytics and downstream AI initiatives.

• • Developed reusable ETL frameworks and scalable data pipelines within Palantir Foundry and Skywise, improving data quality, governance, operational efficiency, and reliability across enterprise data products.

• • Optimized distributed processing through partitioning, predicate pushdown, caching, and query tuning, reducing execution times by 40% while improving platform scalability.

• • Collaborated with engineering and business stakeholders to deliver production-ready data products supporting reporting, business intelligence, and downstream machine learning initiatives.

• • Reduced cloud infrastructure costs by 20% through workload optimization and efficient resource utilization across Azure and GCP environments.

• • Built and maintained automated ETL workflows using Python, SQL, and shell scripting, improving data availability while reducing manual operational effort.

• • Developed API-driven data pipelines integrating multiple business systems into centralized cloud platforms supporting financial reporting and acquisition analytics.

• • Managed Linux-based environments while improving deployment consistency through scripting, automation, and operational monitoring.