Saurabh

Available in Berlin Agentic AI · Data Platforms

AI agents built for the work beyond the demo.

I am Saurabh Shubham, a Data Engineer building production AI products and agentic workflows. My work combines automated execution with deterministic checks, safe deployment, and the platform discipline developed across GROPYUS, Sigmoid, and Amdocs.

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Current role
Data Engineer
Location
Berlin, Germany
Experience
7+ years
Focus
Agentic AI systems

{ 01 · Profile }

Reliable AI begins where the model ends.

I focus on the system around the model: context, tool boundaries, orchestration, validation, observability, and human control. Model output is a proposal to verify, not an answer to trust blindly.

My data-engineering background keeps that work grounded. Dependable pipelines, clean interfaces, and reproducible checks are what turn capable models into useful systems.

{ 02 · Applied AI }

Bounded autonomy. Visible evidence.

Independent product · Live production service

Regulation Check

Product
Built a FastAPI and Supabase PostgreSQL workspace for deterministic EU AI Act screening, tenant-isolated inventories, sourced obligations, tasks, evidence, and readiness reports.
Process
Developed through a Looper-based agentic workflow with scoped milestones, deterministic tests, review gates, and documented operational boundaries.
Delivery
GitHub Actions CI/CD publishes commit-pinned Docker images. The self-healing release path automatically deploys approved releases, retries transient startup failures, verifies production health, and rolls back to the last-known-good release.

Independent project · Pasin — multi-provider agent workflow orchestrator

Reviewable multi-provider execution

Problem
Long-running agent work needs durable scope, safe execution boundaries, and evidence that survives beyond one conversation.
Design
Planned affected paths, provider routing, sandbox controls, deterministic validation, review gates, and rollback-friendly milestones.
Principle
Agents may make progress autonomously; completion remains an evidence-backed engineering decision.

{ 03 · Engineering foundation }

The data systems beneath reliable AI.

Professional experience · GROPYUS

Manufacturing data flows

Problem
Production insight depends on data moving reliably between robotic systems and enterprise platforms.
Contribution
Designed and optimised Python data flows across CDC, lakehouse, and graph-database systems; modelled and orchestrated pipelines with Dagster, Airflow, dbt, and DLT; delivered changes through CI/CD on Microsoft Azure.
Boundary
Internal topology, production data, and unsupported performance metrics remain private.

Professional experience · Sigmoid

Commercial reporting pipelines

Problem
Commercial reporting required repeatable ETL processing across sales datasets.
Contribution
Built and maintained pipelines using Python, PySpark, Airflow, Pandas, Terraform, Google Cloud, and AWS.
Boundary
Client identity, dataset scale, and business-impact metrics are intentionally omitted.

{ 04 · Experience }

From backend integration to data platforms and agents.

  1. 2022—Now

    GROPYUS

    Data Engineer · Berlin

    Manufacturing data platforms across CDC, lakehouse, graph databases, Dagster, Airflow, dbt, DLT, CI/CD, Python, and Azure.

  2. 2021—2022

    Sigmoid

    Software Development Engineer · Bengaluru

    ETL pipelines for commercial reporting with Python, PySpark, Airflow, Terraform, Google Cloud, and AWS.

  3. 2019—2021

    Amdocs

    Software Engineer · Pune

    CRM feature delivery and backend integration using Java, Spring, REST APIs, and SOAP APIs.

{ 05 · Toolkit }

Tools in systems context.

Build

Python · SQL · Java · JavaScript

Move and model

CDC · ETL/ELT · Dagster · Airflow · dbt · DLT · PySpark · Lakehouse

Run

CI/CD · Docker · GitHub Actions · Microsoft Azure · Google Cloud · AWS · Terraform

Store and connect

PostgreSQL · Graph databases · MySQL · MongoDB · REST · SOAP

Build with AI

Claude Code · Looper workflows · Agent orchestration · Context design · Validation gates

{ 06 · Public archive }

Earlier hands-on projects.

My public GitHub includes historical JavaScript and Node.js work such as give-me-a-joke and Image-Crawler. These are archived learning projects—not current Data Engineering case studies or production claims.

Browse GitHub profile

{ Start a conversation }

Agentic AI and data-platform work with substance.

Berlin, Germany · relevant European remote markets

saurabh.friday@gmail.com LinkedIn