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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With over a decade of experience in Data Science, AI/ML Engineering, and MLOps, I’m also dedicated to helping practitioners level up their skills in MLOps, Data Engineering, Machine Learning, and related fields. I share my knowledge in a clear and accessible way to make learning straightforward and actionable.

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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. 👇

Freelance

Big Data, Software Engineering & Domotics

Big Data & Azure Cloud Developper

Liquidity management and optimization
Looking for new business trades and opportunities (Mid & Long term)
Stock screening (Shariah compliant) , monitoring, bench, charting

Islamic Equity Investment (USA/EUROPE)

Société Générale

•	Trainer: As a big data trainer, many training formats where organized for different audience and purpose.
o	Internal/corporate training : produce and present resources (java, scala, spark, hadoop, hbase) for data project teams (56 attendees, 7 sessions) to level up their skills in big data projects (5 days training), in-site.
o	Public training: 2 different training formats, big data fundamentals (1 days training session) and advanced (2 days training session), remotely.

•	Data Project Migration & onboarding: onboard data project to migrate to Azure platform, against establishment data governance (Data Mesh) and other technical constraints (security, scalability, etc.)

•	Data Quality (DQ): Enhance existing App/framework for DQ on the data lake, for each data project,
o	Management of Azure’s stream
	Ensure coherence of azure target solution with on-premise solution, 
	Onboard team to setup cloud native solution (or migrate from on-premise)  to include DQ inhouse solution to their data project.
	Evangelisation and technical workshops

o	Other technical daily tasks
	Setup and maintain airflow workflows (dags, python, livy), 
	monitoring DQ metrics, performances benchmarking with ELK, dashboarding with Kibana,
	Enhance (with features), maintain (bugs, and issues) and integrate (against data governance constraints) DQ solution for Azure and On-premise (ansible, java/scala, microservices in Kubernetes, data mesh, HDI)
support/release.

Technical infra environment: Azure Service EndPoint Policy, Azure App Certificate, Azure HDInsight (3.6 / 4.0), Azure Kubernetes service (ingress, deployment, service, secrets, pvc), CosmosDB, Azure Sql Server, Postgresql, Azure Data Lake Store (Gen2), Queue, Table, Virtual Machine Desktop, Elastic search, Kibana.


Technical soft/dev environment: java, scala, Spark, Spring-boot, Spark/Hadoop, livy, Zepplin, Airflow (Dags in python), swagger, Git, PowerShell / bash.

Azure and Big data developer 

COLAS Digital Solutions - SPEIG

Mission: My main role is to contribute, as a team member, to build up from scratch a set software based on Microsoft (asp.net core mvc) and Cloud Technologies (web apps, functions, sql azure, ServiceBus, etc.) and deliver business features (front and back ends) in agile (Scrum). Some features, are data oriented and, requires to ingest and process data for report delivering. Data concerns: trucks locations, fuel consumption etc. raised by embedded devices [IoT], asphalt production, WattWay [IoT], etc.

Technical environment: .Net, Azure Sql Server, Azure DevOps, Azure WebApp, ASP.NET Core MVC, Templates ARM, Azure ServiceBus, Azure App Service, Azure Data Lake Store, Windows, Git, Team Foundation Server, PowerShell.

Azure developer

Mappy

Mission: Belonging to the BI team, my role is to develop data pipeline, charts and dashboards for requested data insights (servers perf monitoring, Api usage, user behaviors, etc.) for the whole internal teams (Infra, dev, marketing, etc.) and external partners (clients, top management, etc.)
•	Maintaining and implementing:
o	new map/reduce jobs in Hadoop and their tests (java & MrUnit),
o	Spark jobs in Scala,
o	Unit & integration tests,
o	data pipelines in sh scripts,
o	orchestrating data processing jobs with Jenkins jobs and shell scripts;
•	Managing the Data Lake: managing Hive tables (in parquet/csv format) containing logs, perf servers, measures;
•	Indexing data using Elasticsearch;
•	Data viz integration using Kibana, and Tableau, for reporting: servers’ performances, audience, and other KPI;
•	Support for data scientists.

Data Engineer / Data Analyst

SeLoger.com

Mission: Belonging to a transversal team (eCrm), my role is to implement, integrally or partially, data science process, using many different data sources, for Marketing department:
       •	Implementing machine learning algorithms from scratch (collaborative filtering, recommendation system) targeted for geographic department, user segmentation, data featuring, rule based classification;
       •	Implementing and maintain .Net batches (one shot and scheduled) 
              o	for team’ internal use like data extraction (Couchbase, javascript, N1QL), massive mail sending (Neolane),
              o	or marketing business requirement (mail campaigns), etc.;
       •	Setting up data pipelines (with SSIS) and workflows;
       •	Integrating data sources from Couchbase, data bases, flat files, etc. provided by the DMP (kafka, zookeeper) databases and consolidated in the Data Lake (sql server moved to hdfs in Hadoop’ cluster). 
       •	Visual studio add-ins to mutualize internal development patterns and practices.
       •	Benchmarking data aggregation with Flume vs periodic data extraction.

Technical environment: C#, SSIS, sql server, Kafka, Zookeeper, couchbase, windows server, linux, .Net, Git, VS2015, PowerShell, Python, Hadoop (Cloudera), Python Tool for VS, AzureML, Azure Machine Learning Studio, Linux (CentOs).

Data, Machine Learning Engineer 

GDF SUEZ Trading

.Net developer at GDF SUEZ Trading, La Défense, France.
Mission: Develop (within BEE entity) a new app Omega for gas asset management (Sale/Purchase, distribution, optimization, production, etc.). I’m involved as scrum team member for 
•	Maintain and deliver new functionalities.
•	Unit and acceptance automatic tests.
•	Integrating in an internal formula computation engine new math functions.
•	Dev and Integration environment app delivery.

Technical environment: C#, WPF (MVVM), sql server 2008 R2, T-SQL, WCF, Moq, TFS 2010, VS 2010/2015, PowerShell.

.Net Developper

Mutuelle des Architectes Français assurances

In the larger context of decommissioning the old information system, I was enrolled as a .Net developer in two .Net projects (intra and extra net apps) for selling insurance contracts. 
My tasks were 
       1/ to maintain and deliver new functionalities of the above apps as team member, considering technical constraints such as web browser compatibility (IE 7, 8, 9, 10, 11, Chrome, Firefox, Safari) for the MVC web app for instance;
       2/ to provide app support for users targeting different environment: prod, hom, dev, etc.;
       3/ to provide monitoring tools: performance indicators.
Technical environment : c#, ASP.net (MVC 5), Silverlight (MVVM), MEF, EO.Pdf, JQuery 1.10, CSS 3, Html 5, web api 4.0, twitter bootstrapper 2.3, sql server 2008 R2, T-SQL, WCF, data services, Entity Framework 5.0, log4net, Moq, TFS 2012.

.Net developer

Artza Technologies

Charged to manage and lead R&D projects development.
As a manager, i'm in charge to recruit and validate resources to achieve development goals, assign and scheduling user stories to iterations (scrum). 
 
As a tech leader I was enrolled in development process with the roles of Scrum Master and team member. I supervise and participate in scrum iterations: task decomposition, task evaluation, code review, problem solving, architecture design for Rich Desktop and Internet Applications, 1/3 and 2/3 architecture application, IDE integration by Creating Add-ins and Wizards for Visual Studio 2012.

Technical environment : .Net 4.5, WPF, WCF, MVVM, EntLib, Prism, EF 5, Silverlight, ASP.Net, MVC 4, RIA Services, C#, T4 templates, EnvDTE for VS 2012

R&D Director

Groupe ADP

Price fixed Project : implementing an Airport Resources Manager.
• Technical architecture : 2-Tiers Architecture, WCF (dual binding), client WPF ( .Net 4.5, Prism, MVVM, Unity), server (console, service windows, Win server 2008), Oracle 11.2 ( Entity Framework 5.0, Oracle Data Provider).
• Development méthodology: Scrum, TDD (MSTest, Moq, TestRunner, tfspreview).
• Writing technical document : software architecture, technical environment, deployment process, etc.
• Supervising functional documents. 
• Technical refering in a team of 4 developers.

.Net Developer

Banque de France

• Develop,  maintain and package ( Item templates , project templates and vsix) a shared technical framework (based onUnity et EntLib) for building Rich applications such as patterns [ MVC (ASP.NET), MVVM (WPF), SOA, IoC, Composite Applications, muti-threading, data provider, dual binding (for WCF), etc.], and windows docking manager (AvalonDock) for WPF apps.
• Technological foresight related to framework development practices.
• Technical lead assisting project managers at differents steps of the live cycle project developement.
• Monitoring and managing .net projects in Dev, Hom et UAT environment withing Sofact/Bamboo.
• CTI member advising and validating project managers and project team members during the software design phase.
• Tools and software benchmark.

.Net Software Architect

TL;DR

Working remotely ( 90% of time)

2016 -> Now: Software / Data (Big Data) / Azure Engineer
2010 -> 2016: worked as software engineer (Essentially Microsoft)
2006 -> 2010: work as R&D engineer

A bit more:
Since 2016, I am working on big-data/Azure projects as data engineer for massive data processing, data quality, and and data migration projects.

I moved to the software industry working on different sectors (stock market, financial institution, Transport, Insurance, building, energy, media) as a software developer (essentially in .Net). 

Got my PhD (in data mining) in  2011; I worked essentially on designing a specific statistical data analysis Model (called Symbolic Data Analysis, SDA). combined with, relational algebra, nested algebra. Hence to the algebraic soundness and completeness of the model a set of Statistical model algorithms (k-means and hierarchical clustering and other data mining / Machine learning) were designed with simple queries. All this were embedded in SQL SERVER using C#

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Aurimas Griciūnas

🇵🇸 Omar Merroun, PhD 🇵🇸

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Aurimas Griciūnas

🇵🇸 Omar Merroun, PhD 🇵🇸

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