"When learning meets data, growth becomes measurable and inevitable."

Applied AI Engineer | Forward Deployed Engineer | Data Analytics | Data Engineering | AI Systems

As a Forward Deployed Engineer my role is to provide AI backed solutions to legacy Enterprise Systems across ANY TECH STACK as needed by clients.

It could be Google Cloud, Databricks, AWS, Snowflake, Agentic solutions and more.

Building end-to-end AI solutions across ETL, analytics, machine learning and AI engineering

Current Projects:

• 🛡️ Databricks Fraud Detection — streaming fraud pipeline on the Databricks lakehouse: Auto Loader replays 6M PaySim mobile-money transactions into a Unity Catalog medallion layout, with duplicate, late, schema-change and malformed-data scenarios each proven by SQL checks; a Lakeflow declarative pipeline of materialized views builds Silver with data-quality expectations, a quarantine shelf, duplicate removal and leak-free point-in-time velocity features; job and pipeline deployed as code via Databricks Asset Bundles; Bronze and Silver verified end to end on Databricks Free Edition (v0.2.0) with every count exact and incremental refresh measured, 79 tests in CI — next: ML training and scoring
• 🖱️ AWS Clickstream Analytics — serverless clickstream analytics lake on AWS turning 165,474 real web clicks into product and funnel insight: Lambda ingest → S3 Bronze/Silver/Gold → AWS Glue (PySpark) → Glue Data Catalog → Athena, with quality gates that quarantine bad rows instead of dropping them and a Gold star schema for BI; the whole stack is infrastructure as code (AWS SAM) with encryption, concurrency, timeout and query-scan cost guardrails enforced by tests; Spark output reconciles row-for-row with the source on every click, 123 tests in CI — next: first AWS deployment (Mumbai), then streaming and Redshift
• 🏗️ Sales Data Pipeline — production-grade Medallion (Bronze → Silver → Gold) ETL pipeline with automated data quality gates, feature engineering, and an embedded DuckDB analytical store; orchestrated via a self-hosted Apache Airflow DAG (9 independently retryable tasks) syncing into a partitioned/clustered BigQuery cloud warehouse, live-verified end-to-end against real GCP infrastructure; FastAPI query layer published to GHCR; watermark-based incremental load (insert-only CDC) now live, 160 tests enforced in CI — next: MLflow/W&B run tracking (v2.0)

I'm a data-driven professional passionate about applying AI, Data Engineering and Analytics to improve Business, Learning and Development (L&D) outcomes.

After a successful career in Aviation training and Airport operations, I've transitioned toward AI Engineering, data engineering and data analytics, where I can apply analytical methods to solve learning and business problems.

I build data-driven solutions covering:

• AI/ML Engineering — end-to-end training pipelines, inference APIs, and production cloud deployment
• Cloud Data Engineering — GCP Cloud Run, Artifact Registry, BigQuery; containerised CI/CD
• Lakehouse Data Engineering — Databricks Auto Loader, Spark Structured Streaming, Delta Lake, Unity Catalog, Asset Bundles
• Snowflake Data Cloud — Dynamic Tables, native Semantic Views, and Cortex Agents/Analyst for governed natural-language analytics
• Observability — structured JSON logging (Cloud Logging), Prometheus metrics endpoints
• ETL pipelines and data workflows
• Exploratory data analysis and visualization
• Predictive modelling using Python and R
• Analytics dashboards and reporting systems (R Shiny, Tableau, Looker Studio)

Programming & Analysis:

ML Engineering & APIs:

Visualization & Reporting:

Cloud Data Engineering:

Lakehouse & Streaming (Databricks):

AWS Data Engineering:

Data Warehousing & Cortex AI:

Issued by Snowflake

The Sales Data Pipeline evolves from a production-grade Medallion ETL into a full customer analytics platform — unifying transactions, segmentation, and retention into a single source of truth.

flowchart TD
    subgraph Sources["📥 Data Sources"]
        S1["CRM"] & S2["POS / Transactions"] & S3["Web Analytics"]
    end

    subgraph ETL["🏗️ Medallion ETL  ✅  Built — Python · Pandas · Pydantic"]
        B["🥉 Bronze — Ingest + schema validation"]
        C["🥈 Silver — Clean · Dedup · Feature engineering"]
        D["🥇 Gold — Star schema · AOV · CLV pre-aggregated"]
    end

    subgraph Infra["⚙️ Data Infrastructure  🔄  In Progress — Airflow ✅ · BigQuery ✅ · Incremental load ✅"]
        ORC["Apache Airflow ✅<br/>Self-hosted DAG · 9 tasks · Docker Compose"]
        WH["BigQuery ✅<br/>Partitioned · Clustered · Live-verified"]
    end

    subgraph Seg["🧠 Customer Segmentation  🔜  Planned — scikit-learn · Databricks"]
        E["RFM Analysis<br/>Recency · Frequency · Monetary"]
        F["Cohort Analysis<br/>Signup cohorts · Engagement lifecycle"]
        G["K-Means Clustering<br/>Unsupervised persona discovery"]
    end

    subgraph Ret["🔁 Retention Analytics  🔜  Planned — scikit-learn · Databricks"]
        H["Churn Classification<br/>At-risk flagging · Re-engagement triggers"]
        I["LTV Correlation<br/>High-value segment identification"]
    end

    subgraph Serving["⚡ Serving Layer  ✅  Built — FastAPI · DuckDB · Docker"]
        J["🦆 DuckDB — In-process analytics"]
        K["FastAPI REST API"]
    end

    subgraph Dash["📊 Analytics Dashboard  🔜  Planned — Tableau · Streamlit"]
        L["KPI tracking · Segment views<br/>Retention curves · LTV by cohort"]
    end

    subgraph Agent["🤖 Conversational Analytics Agent  🔜  Planned — LangGraph · Claude Sonnet 5"]
        M["Text-to-SQL Tool-Calling Agent<br/>Natural-language queries over the warehouse"]
    end

    Sources --> B
    B --> C
    C --> D
    D --> ORC
    ORC --> WH
    D --> J
    WH --> E
    WH --> F
    E --> G
    G --> H
    F --> H
    H --> I
    J --> K
    I --> K
    K --> L
    L --> M

• Currently enrolled in IIT Delhi — Advanced Certification: Forward Deployed Engineer
• Google Data Analytics Professional Certificate
• IBM Data Analytics Professional Certificate with Excel & R

My mission is to bridge Data Engineering and Learning — using data to make learning, Business Analysis and training more effective.

📍 Mumbai, India
📧 [email protected]
🔗 LinkedIn
💼 GitHub Projects

"When learning meets data, growth becomes measurable and inevitable."