- I���m an AI/ML Engineer with ~5+ years spanning data science, analytics, and GenAI across healthcare, fintech, and enterprise platforms.
- I build LLM-backed applications with RAG, embedding search (pgvector/PostgreSQL), and evaluation to reduce hallucinations.
- Comfortable across AWS, Azure, and GCP—deploying scalable, cloud-native ML/GenAI services.
- Strong background in classical ML (XGBoost, GLM, ARIMA) and BI dashboards for decision support.
- I’m currently focused on fine-tuning/evals, safety/guardrails, and production-grade RAG.
Languages & ML: Python, scikit-learn, XGBoost, Statsmodels, Pandas, NumPy, PyTorch/TensorFlow (working knowledge)
GenAI & NLP: Prompt engineering, OpenAI/Azure OpenAI, AWS Bedrock, Embeddings, RAG, LangChain/LangGraph (working knowledge)
Data & Storage: PostgreSQL + pgvector, SQL, Azure Synapse, Redshift, S3, ETL/SSIS
Cloud & MLOps: Azure Functions/App Service, AWS (Glue, Redshift, SageMaker basics), GCP (Vertex AI basics), Docker, CI/CD
Analytics & BI: Power BI, Tableau, Jupyter, Real-time dashboards/automation
Big Data/Streaming (exposure): Spark, Kafka
Observability: Application Insights, logging/monitoring for ML services
- RAG pipelines with pgvector + PostgreSQL for domain-grounded Q&A
- LLM evaluation frameworks (context relevance, faithfulness, toxicity) and hallucination reduction
- Cloud-native deployment of AI apps using Azure/AWS services, with cost/perf trade-offs
- Agentic workflows for enterprise process automation (safe tool-use, retrieval, structured output)
- M.S., Information Technology – Wilmington University (May 2025)
- Built/maintained LLM chatbot with RAG using Azure OpenAI, pgvector/PostgreSQL, Azure Functions/App Service, Docker
- Experience across healthcare analytics, fraud/risk scoring, readmission prediction, and operational dashboards
📬 Email: nh13@iitbbs.ac.in
🔗 LinkedIn: Harish Chowdary
💻 GitHub: Harish-34
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