Building practical, reliable AI systems for enterprise use cases.
I'm a Senior AI/ML Engineer at Tiger Analytics, with an engineering background spanning data engineering, MLOps platforms, enterprise agentic AI, and backend/SDK development.
My work has evolved from large-scale data pipelines and ML platform capabilities to building agentic AI platform components, Python/FastAPI services, secure integrations, and reusable engineering foundations. I've also had experience leading a small engineering team.
I'm interested in the next step of that journey: Applied AI Engineering, Forward Deployed Engineering, and AI Solution Architecture — connecting technical execution with system design and real business requirements.
| Role | Focus |
|---|---|
| Senior AI/ML Engineer · Tiger Analytics | Enterprise agentic AI, FastAPI backend services, authentication and authorization, reusable SDKs, integration patterns, and small-team leadership |
| Machine Learning Engineer · Tiger Analytics | MLOps platform engineering, data pipelines, cost analytics, and supporting ML lifecycle capabilities |
| Associate Software Developer · IQVIA | Healthcare data engineering, legacy-to-big-data modernization, Spark-based processing, ETL, and pipeline orchestration |
| Area | Technologies & capabilities |
|---|---|
| Agentic AI / LLM tooling | Agent workflows, LangChain, LangGraph, tool integration, MCP and RAG concepts (hands-on depth varies) |
| Backend & integrations | Python, FastAPI, REST APIs, SDK development, authentication, authorization, enterprise integration |
| ML platform experience | MLOps platforms, model lifecycle exposure, data and cost analytics pipelines |
| Data engineering | SQL, Scala, Apache Spark, PySpark, Apache Airflow, Hadoop, ETL and data pipelines |
| Cloud & engineering tools | Azure and Databricks experience; AWS/GCP exposure; Git and GitLab |
Currently strengthening: LLM evaluation, AI guardrails, observability, production deployment, cloud architecture, and customer-focused solution design.
- Agentic AI platform engineering: Backend functionality and secure integration capabilities for enterprise agent applications.
- Reusable AI SDKs: Common integration components and standardized approaches to support AI engineering teams.
- ML platform engineering: Data and cost analytics pipelines supporting MLOps-related operations.
These are high-level summaries of professional experience, not links to public employer code.
Recipe Generation from Food Image
A deep-learning-based application exploring the generation of recipe details from food images. Includes a Python application and work using pretrained components.
➡️ Browse all public repositories
Personal AI engineering projects focused on secure agent backends, LLM evaluation, observability, and deployable enterprise-style AI solutions. These are learning and portfolio goals, not claims of completed public projects.
- Databricks Certified Machine Learning Associate · Sep 2025
- Databricks Certified Data Engineer Professional · Jun 2025
- Azure Databricks & Spark for Data Engineers (course) · Dec 2023
- Microsoft Certified: Azure Data Fundamentals · Jan 2023
- IBM Data Science Professional Certificate · Aug 2021
I'm interested in enterprise AI engineering, applied AI systems, agentic architectures, and solution engineering.
From data platforms to AI systems — always learning, building, and improving.