Aurimas Griciūnas

Senior Solutions Architect

SwirlAI

Driving MLOps, Data Engineering and Machine Learning industry forward by helping professionals Level Up in these disciplines.

Founder & CEO

neptune.ai

Chief Product Officer

Head of Product

Driving MLOps industry forward by helping Neptune customers succeed in adopting MLOps best practices.

Transactie Monitoring Nederland (TMNL)

- Contributed to the development of end-to-end Machine Learning platform capabilities in a highly secure environment.
 - Focused on leveraging AWS SageMaker and supporting AWS services to enable scalable experimentation and model deployment.
 - Collaborated with the team to build reusable template libraries, streamlining the process of running ML experiments and deploying models efficiently and securely.

Senior MLOps Engineer

HomeToGo

- Led cross-functional teams of Cloud Engineers and Data Platform Engineers, driving key infrastructure and data initiatives.
 - Spearheaded a company-wide Cloud-Native Transformation initiative to modernize applications and infrastructure.
 - Championed the migration to Infrastructure as Code (IaC), standardizing infrastructure provisioning using Terraform for improved scalability, consistency, and automation.

Data Architect / Data Platform & Cloud Engineering Lead

- Led a Team of Data Engineers.
- Led a Team of Data Platform Engineers.
- Initiated and led a successful company wide Cloud Native Transformation.
- Migrated entire Data Platform Stack to Kubernetes. This included:

        - Airflow.
        - Superset.
        - Spark.
        - MLFlow.
        - Flink.
        - Airbyte.
        - DataHub.

- Introduced first Stream Processing applications that moved some of the Data Warehouse Processing stages upstream the pipeline.

Senior Data Engineer / Data Platform Team Lead

- Led a team of Data Engineers.
- Initiated and led a Data Warehouse migration from Redshift to Snowflake effort. It also included a refactoring of the Data Model.
- Oversaw Data migration from a Druid cluster to Redshift and later Snowflake.
- Introduced and rolled out Machine Learning Experiment Tracking System.
- Deployed and maintained first on-prem Kubernetes Clusters in the company.

Senior Data Engineer / Team Lead

- Led a successful implementation and optimisation of a custom Website Activity Tracking Ingestion Pipeline.
- Deployed and rolled out a company-wide Airflow Cluster.
- Led a full migration from Jenkins to Airflow for Data Engineering Pipelines.

Senior Data Engineer

Tesonet

Responsible for end-to-end development of ML-based products and big data solutions:

 - Identified business use cases suitable for machine learning and translated them into technical solutions.
 - Designed and implemented the full ML lifecycle, including data extraction, cleaning, validation, model training, packaging, and deployment.
 - Contributed to the architecture and development of scalable big data ingestion pipelines.
 - Set up and configured Kafka topics; developed real-time processing applications using Kafka Streams and Spark.

Senior Data Scientist/Engineer

Danske Bank

Projects I worked on:

-Affordability model enhancement using transaction data.
-Bringing transaction data into Balance Sheet stress-testing.
-Unlocking potential of transaction data throughout Risk Management function.
-Stress-testing of REA components and dynamic analysis on different credit portfolio levels.
-Process optimization and automation. Preparing models for production placement.

Senior Data Scientist/Analyst

Projects I worked on:

-Retail customer affordability project in Big Data analytics team.
-IFRS9 Life-time probability of default (LTPD) model development.
-IFRS9 Prepayment model development.
-IFRS9 impact on P&L analysis.
-IFRS9 and pre-IFRS9 ECL stress-testing and reporting.
-REA stress-testing and dynamic analysis on different credit portfolio levels.
-Process optimization by both improving old approaches and introducing new technologies.

Analyst

ADB Gjensidige

Was responsible for implementation of Solvency2 regulatory standards and providing required reports.

Actuary

Was responsible for generating business analysis reports for management.

Was responsible for calculation of Technical Reserves for Non-Life insurance products by applying different triangulation methods.

Junior Actuary

Follow me to learn about Data Engineering and ML Systems | Author of SwirlAI Newsletter | Public Speaker

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Leading Product Strategy.

TOON (Token-Oriented Object Notation) is out for some days now and it aims to make communication with LLMs more accurate and token-efficient.

The TOON topic is now one of the hottest news on the LLM market and it might actually matter.

𝗪𝗵𝘆 𝗜 𝘁𝗵𝗶𝗻𝗸 𝘀𝗼:

I was initially hesitant to cover this, potentially being another hype to quickly fade, but:

✅ The format has been shown to increase the accuracy of models while decreasing the token count. I was not sure if there were any accuracy retention studies made, it seems there were.
✅ Token efficiency is extremely important when working with Agentic Systems that require a lot of structured context inside of their reasoning chains. And we are moving towards a post-PoC world where there is a lot of emphasis placed on optimisation of the workflows.

𝗔 𝘀𝗵𝗼𝗿𝘁 𝘀𝘂𝗺𝗺𝗮𝗿𝘆:

- Token-efficient: typically 30-60% fewer tokens on large uniform arrays vs formatted JSON.
- LLM-friendly guardrails: explicit lengths and fields enable validation.
- Minimal syntax: removes redundant punctuation (braces, brackets, most quotes).
- Indentation-based structure: like YAML, uses whitespace instead of braces.
- Tabular arrays: declare keys once, stream data as rows.

An example:

𝘑𝘚𝘖𝘕 𝘧𝘰𝘳𝘮𝘢𝘵:

"shopping_cart": [
    { "id": "GDKVEG984", "name": "iPhone 15 Pro Max", "quantity": 2, "price": 1499.99, "category": "Electronics" },
    { "id": "GDKVEG985", "name": "Samsung Galaxy S24 Ultra", "quantity": 1, "price": 1299.99, "category": "Electronics" },
    { "id": "GDKVEG986", "name": "Apple Watch Series 9", "quantity": 1, "price": 199.99, "category": "Electronics" },
    { "id": "GDKVEG987", "name": "MacBook Pro 16-inch", "quantity": 1, "price": 2499.99, "category": "Electronics" }
  ]
}

𝘞𝘩𝘦𝘯 𝘦𝘯𝘤𝘰𝘥𝘦𝘥 𝘪𝘯𝘵𝘰 𝘛𝘖𝘖𝘕 𝘧𝘰𝘳𝘮𝘢𝘵:

shopping_cart: items[4]{id,name,quantity,price,category}:
    GDKVEG984,iPhone 15 Pro Max,2,1499.99,Electronics
    GDKVEG985,Samsung Galaxy S24 Ultra,1,1299.99,Electronics
    GDKVEG986,Apple Watch Series 9,1,199.99,Electronics
    GDKVEG987,MacBook Pro 16-inch,1,2499.99,Electronics

𝗥𝗲𝘀𝘂𝗹𝘁:

✅ 43% savings in token amount.
✅ Directly translates to 43% savings in token cost for this LLM input.

❗️ Be sure to know when NOT to use the format (and always test it for your application specifically):

- Deeply nested or non-uniform structures.
- Semi-uniform arrays.
- Pure tabular data.

ℹ️ I will be testing it in the upcoming weeks.

Let me know if you have already tested TOON and what are your takeaways! 👇

Founder @ SwirlAI • UpSkilling the Next Generation of AI Talent • Author of SwirlAI Newsletter • Public Speaker

𝗠𝗖𝗣 plus 𝗔𝟮𝗔, here is how they complement each other 👇

Protocol wars continue to rage, let's understand how Googles A2A (Agent2Agent) protocol is different from MCP and how they complement each other (read till the end).

𝘔𝘰𝘷𝘪𝘯𝘨 𝘱𝘪𝘦𝘤𝘦𝘴 𝘪𝘯 𝘔𝘊𝘗:

𝟭. MCP Host - Programs using LLMs at the core that want to access data through MCP.

❗️ When combined with A2A, an Agent becomes MCP Host.

𝟮. MCP Client - Clients that maintain 1:1 connections with servers.
𝟯. MCP Server - Lightweight programs that each expose specific capabilities through the standardised Model Context Protocol.
𝟰. Local Data Sources - Your computer’s files, databases, and services that MCP servers can securely access.
𝟱. Remote Data Sources - External systems available over the internet (e.g., through APIs) that MCP servers can connect to.

𝘌𝘯𝘵𝘦𝘳 𝘈2𝘈:

Where MCP falls short, A2A tries to help. In multi-Agent applications where state is not necessarily shared

𝟲. Agents (MCP Hosts) would implement and communicate via A2A protocol, that enables:

➡️ Secure Collaboration.
➡️ Task and State Management.
➡️ User Experience Negotiation.
➡️ Capability discovery - similar to MCP tools.

𝗛𝗼𝗻𝗲𝘀𝘁 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀:

❗️ Open protocols for Agent communication are important but they are just a piece of the picture, there will be a need for standards that govern all of the existing protocols and other missing pieces. E.g.

❓ How do we standardise tracing and Observability in multi-agent IOA systems?
❓ How do we retain the identity of the running job of an AI Agent instance if the communication standard is not unified in different parts of the pipeline?
❓ ... 

✅ More on this in my future posts, stay tuned!

Let me know your thoughts in the comments. 👇

Nationale-Nederlanden

Nationale-Nederlanden (NN) is a financial services company active in 11 countries. It provides retirement services, pensions, insurance, banking and investments to approximately 18 million customers.

I have been involved in the NNDAP project (NN Data Analytics Platform). NNDAP is a microservices based software product that aims to provide a data platform blueprint for every business unit of NN, maximising efficiency and minimising repetitive work.

With NNDAP, business units can easily provision a brand new data platform and deploy modules to it, such as a Data Extraction, Data Ingestion, Data Orchestration, Data Harmonization, Data Quality, oAuth, among others. All the modules can be flexibly configured via config-as-code, making sure deployment from development all the way to production is a breeze. Furthermore, NNDAP is proudly part of the 'Innersourcing' initiative (open-source within NN), allowing all engineers to contribute to the codebase of NNDAP.

Senior Data Platform Engineer

Rabobank

Set-up, development and maintenance of the Enterprise Data Lake for Rabobank in order to have an efficient, structured and reliable Enterprise Data Lake. This team is specialized in data governance topics, for the most part data quality and data catalogue integration. The Data Governance team provides self-service data governances capabilities and ensures integration with the data catalogue to enable that data is controlled, findable, understandable, accessible and meets regulations. We build solutions to comply to i.e., GDPR and define and develop solutions together with the team surrounding data quality checks.

Experience:

Strong knowledge about DevOps process (Git, CI/CD, YAML pipelines)
Strong coding skills (Python, FastAPI, Pytest)
Airflow
Good knowledge of Bash or PowerShell 
Good knowledge of Azure services ADLS gen2
Azure Data Factory (Pipelines & Data Flow)
Azure SQL server
Log Analytics Workspace
Security (Virtual Networks, Firewall and IAM)
Azure App Service
Azure Function
Azure Resource Manager Templates (ARM)
Bicep
Azure Active Directory
Power BI Dashboarding
Debugging Azure services from Activity Logs, Metrics, etc.
Experience with Data Modeling 
Data Warehouse Schema Design - Dimensional Modeling and Star Schema
Experience with Data Management topics like Data Governance, Data Privacy and Data Access
Scrum and DevOps methodologies

Data Platform Engineer (Data Governance)

FRISS

Helping to build solutions against fraud with: (ElasticSearch + FastAPI + Azure + Python + Graph/Network Analytics)

I basically work on 3 projects:

* Network Entity Lib: This is an Azure Artifact project, which provides a pip python package. It consists of a set of unified data models which are used internally in FRISS Products. It contains a representation of an Entity Graph (set of Entities and Edges), that we call FRISS Standard Model.

* Network ETL: Python application which transforms claims data in FRISS standard model (SDM) to a data format suitable to the network stack. It can be run in 2 modes:

- 1. Realtime: data in SDM format comes in via Kafka stream in real time.
- 2. Historical Load: A whole history of data in the form of blob documents from Azure Data Lake Storage is extracted, transformed and loaded into ElasticSearch database. This is either done when an existing Tenant will use the network stack for the first time or data needs to be transformed due to a change in the data model (Network Entity Lib)

* Network API: Python FastAPI application that serves the network viewer, network detection check, network claim history. It provides endpoints for searching, detection, expanding networks, storing tenant config and managing lists.

Senior Python Engineer (Fraud Analytics)

ABN AMRO Bank N.V.

Builds systems and models, which help the business to calculate, predict and prevent risks for internal and external customers.

* Gathering requirements from the business and refining them;
* Analysing the data;
* Modify  Credit Risk models developed by Data Science team and deploying them to Azure DevOps pipeline;
* Developing and adding new functionalities in Python which will enable the bank to better determine Financial Risk and create detailed automated Excel reports (internal and external reporting) with Python and openpyxl for Financial Auditing;
* Working in a DevOps way by taking over activities of other colleagues, like testing, implementing and production support;
* Support SAS team regarding Credit Risk models by providing required datasets and helping with functional tests.
* Help rewrite credit risk model features from Java code to Python
* Write and modify SQL queries on Oracle database according to business needs.

Python Software Engineer  (Risk Management)

Protolabs

I work building Supply Chain and Logistics features for 3D Hubs platform:

- Integration with Customer.io and Segment to send notification to clients when a certain event happens. Set Up a Task Queue with Celery and RabbitMQ
- Help transition Logistic process for European customers from Drop-ship model to Cross-Docking model.
- Interact with Auction allocation/bidding algorithm
- Create SQLAlchemy event listeners in order to help the Data Engineering team to gather useful information, mostly related to statuses timestamps.
- Surface backend features in the frontend: enable users to order 3D Hubs boxes and toggle CNC and 3D printing capabilities.
- Backfilling of data for new properties/columns created in database models.
- Enable Manufacturing capabilities, like CNC machining when new supplier is added to the platform (Technologies, Processes, Materials).
- Create API endpoints to be consumed in the fronted project.
- Architecting Stores with NgRx: build the entities that make the store, setting up the entity adapter, the actions, effects and the reducers for each entity.
- Add ability to view, edit, delete, create entities and properties in FlaskAdmin
- Work on New Order Manager Info component in order to show Shipping information to the user according to Order status.

Full Stack Engineer (Digital Manufacturing & Supply Chain)

Ericsson

I worked as a software developer contractor hired by Neueda, allocated in Ericsson site in Athlone, Ireland. I helped building backup and restore features in order to protect Ericsson’s clients data, which is related to the product Ericsson Management as a Service (ENMaaS). 

It basically evolves the following steps: encrypt, compress, decrypt, decompress and process the files while also having concerns about the performance, using Python multiprocessing library. Most of the commands sent to the servers were performed using the the Python subprocess module and bash commands. We used the rsync protocol, to transfer file volumes.

Regarding code quality, we used the Unittest framework and Mock library for Unit Tests and we used pylint and flake8 all configured in a tox automation file in order to check possible bugs, and style misconducts.

Regarding the versioning of the code we used Git and Gerrit Code Review and finally, for project management tracking, we used Jira aligned with Scrum methodology (daily meetings, retrospective and grooming).

Python Developer

Magazine Luiza

Grupo Mulheres do Brasil (Group Women of Brazil)" aims fostering female protagonism in several important areas for the development of Brazil as entrepreneurship, health and education. I helped them with the following:

- Redesign their  website: www.grupomulheresdobrasil.org.br.
- Manage product backlog.
- Set up payment system, so that the organization could receive donations.
- Participate in meetings with the stakeholders (Group Women of Brazil) in order to understand their needs and plan a strategy to achieve their goals.
- Organize database information for their intranet system and the deployed website.
- Perform migration operations and cleaning of the data.

Product Owner / Software Developer

Serasa Experian

I worked on the project Polis, I helped building a web API composed of functionalities to show the commute time among locations in the city of São Paulo, mobility index map, among others. The commute time was computed based on data collected and analyzed from SPTrans and Google Maps APIs. I used the following technologies: AWS, Python, Docker, Flask, Postgresql, Google Maps API. The frontend of the application used AngularJS 1, HTML5, CSS3 and Bootstrap and it basically showed the representation of the data processed in a meaningful way to the users.

Summer Job - Data Science/Engineering

Universidade de São Paulo

I worked with the subject Probability and Statistics. The Teaching Assistant Program (PAE) permits the active participation of Graduate Students in undergraduate teaching activities in courses offered by The University of Sao Paulo. The program offers the students the opportunity to train their teaching abilities in undergraduate classes under the supervision of experienced university teachers, preparing them for a future teaching career.

Teacher Assistant (Probability and Statistics)

Upwork

Worked with technical transcription and data entry for the company Pactera in order to help to improve the Skype voice recognition software.

Data Entry Specialist

* I want to be alive to see Python 3.14159. Wouldn't that be PieThon... or PiThon?

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