Solutions
Four areas where I take a client from idea to a running, monitored system.
GenAI and multi-agent systems
RAG pipelines, natural-language-to-SQL, and multi-agent orchestration served through FastAPI microservices, with LLM tracing and evaluation.
Data engineering
Batch and streaming pipelines, data lakes and warehouses, with quality checks and cost control built in.
GPU and HPC infrastructure
Cluster scheduling and parallel storage for training and heavy analytics: Slurm, Lustre and other parallel file systems, RAID, JupyterHub, and GPU workloads.
Platform, CI/CD and FinOps
Containerized microservices on Kubernetes, automated build and release pipelines, and cloud cost tracking for warehouses and AI workloads.
Data engineering and AI dashboards
The two views I build for client teams: pipeline health for data engineers, and model and GPU health for AI teams.
Sample values for illustration. They show the layout and metrics I deliver, not a live client system.
SaaS product: AI analytics platform
Ask a question in plain English and get an answer from your database. Built full stack, deployed on AWS.
10,000+ concurrent requests · 99%+ uptime · CI/CD from commit to deploy
Projects
Open-source and client-style builds.
friendlyetl
Zero-code ETL framework on PyPI with pipeline chaining, deduplication and validation.
50+ GitHub stars · 50,000+ records per run
U.S. immigration pipeline
PySpark and Hadoop HDFS with a star schema, orchestrated by Apache Airflow.
Pricing and segmentation engine
K-Means customer segments with live Matplotlib dashboards.
GCP data lake
Centralized lake feeding Snowflake and BigQuery, with monitoring and cost optimization.
Experience and ways of working
I have served multiple clients as a data engineer, and I now lead AI engineering. I work in Agile Scrum sprints with Kanban boards, so vendors and clients see progress every week.
Team Lead, AI Engineering
- Lead the AI engineering team through Scrum sprints and Kanban boards, from requirements to production release.
- Deliver LLM, RAG and multi-agent solutions with FastAPI microservices, CI/CD pipelines and cloud deployment.
- Work directly with clients and vendors to scope work, report progress and keep both sides satisfied.
- Review code, mentor engineers and set standards for testing, tracing and cost control.
Technical Mentor
- Mentored 15+ engineers on OOP, data structures and algorithms, and system design.
- Resolved pipeline and data errors for junior data engineers in AWS and GCP big data environments.
- Trained developers on Snowflake, BigQuery, data lake architecture, PySpark, Kafka, Airflow, Docker, Kubernetes, Cassandra, HBase and dbt.
Data Engineer
- Designed a scalable GCP data lake for central storage and processing.
- Built ETL/ELT pipelines into Snowflake and BigQuery that raised data availability and cut latency.
- Automated batch and streaming workflows with Cloud Storage, Dataflow, Pub/Sub and Composer.
- Added data quality checks and monitoring, which led to 40% fewer data quality issues.
- Improved query performance by 30–35% with indexing and schema optimization, and lowered Snowflake and BigQuery cost through warehouse and slot management.
Junior Software Engineer
- Designed and optimized SQL queries for large-scale assessment datasets.
- Developed automated ETL scripts in Python and Apache Spark for analytics pipelines.
Education: B.Tech, Artificial Intelligence and Data Science, CARE College of Engineering. Recognition: LinkedIn Top Voice, HackerRank SQL Gold and Python Silver, GitHub Pull Shark, PyPI publisher.
Open source on GitHub
Public projects and contributions, pulled live from GitHub.
Writing and community
I write about AI and data engineering on Medium, and I’m an active member and technical mentor in the Google Developers Group community.
Ready to build something?
Tell me the problem and your timeline. I work with clients and vendors to deliver, and I’m open to AI engineering and data engineering roles.