Tomáš Jeníček

Prague · DevOps Platform Lead

Tomáš JeníčekDevOps & AI Platform Consultant

I help build secure, automated and AI-enabled platforms for modern software delivery.

Platform Engineering · GitHub Enterprise · AI-Assisted Software Delivery · Security

More than 20 years of experience in IT. I specialise in DevOps platforms, GitHub Enterprise, automation, developer experience, software supply-chain security and the practical use of AI in the day-to-day work of development teams.

Get in touchLinkedIn
Tomáš Jeníček – painted portrait in an astronaut helmet and space suit against a nebula
years in IT
20+
leading a DevOps platform team
Platform Lead
working languages
CZ / EN

DevOps

The infinite delivery loop

Planning, building, deploying and operating never stop. The platform makes that loop faster, safer and measurable.

Continuous IntegrationContinuous DeliveryPlanCodeBuildTestReleaseDeployOperateMonitor
Diagram of the CI/CD loop shaped as an infinity sign: Plan, Code, Build, Test, Release, Deploy, Operate, Monitor.

About

A platform that makes software delivery easier for teams

I have worked in IT for more than twenty years, across enterprise IT, platform engineering, software delivery and data management. Today I lead a DevOps platform team that builds a secure, scalable and developer-friendly platform. Its purpose is to help teams deliver high-quality software faster.

My current focus is a modern development platform built around GitHub Enterprise, secure CI/CD, automation and AI-assisted development. I lead the migration to GitHub Enterprise with Data Residency, the rollout of GitHub Actions and managed runners, GitHub Advanced Security, Copilot adoption with usage analytics, custom AI model integrations and secure MCP-based developer services. I am interested in making AI a governed, measurable and secure part of the platform, not just an individual productivity tool.

I design and improve the toolchain across GitHub, Azure, OpenShift and Kubernetes, Docker and Podman, Terraform, Artifactory, SonarQube, feature toggling and observability. I also build data-driven insights into platform usage, delivery performance and AI adoption, so that decisions rest on evidence.

I started in Linux and Windows enterprise administration, database architecture and administration, data modelling, ETL, scripting and PL/SQL. That foundation still shapes how I think about reliable platforms, automation and integration.

Experience

Career path

From system and database administration through DevOps to leading a platform team.

  1. – present

    DevOps Platform Lead

    Banking

    I lead the DevOps platform team responsible for the evolution, reliability, security and developer experience of the enterprise software delivery platform. I set the platform direction: GitHub Enterprise Cloud with Data Residency, GitHub Actions and managed runners, GitHub Advanced Security, GitHub Copilot adoption, secure AI and MCP integrations and analytics of their use.

    • GitHub Enterprise Cloud
    • GitHub Advanced Security
    • Copilot
    • MCP
    • OpenShift
    • Terraform
    • Azure

  2. –

    Senior DevOps Engineer

    Banking

    Design, automation and operation of enterprise development platforms. I led key automation and infrastructure initiatives on OpenShift for business-critical systems including internet banking, and built secure, maintainable CI/CD pipelines integrated with SonarQube and Artifactory.

    • GitHub Actions
    • Jenkins
    • OpenShift
    • Terraform
    • Azure CDN
    • SonarQube
    • Artifactory

  3. –

    Senior DevOps Engineer, DevOps Team Lead

    Financial sector · freelance

    I led the DevOps platform team responsible for adopting, operating and improving the tooling around OpenShift Container Platform. I designed CI/CD pipelines for OpenShift-based projects and supported business-critical systems including internet banking. The team worked in an agile way, with a focus on knowledge sharing and ownership.

    • OpenShift
    • CI/CD
    • Jenkins
    • Docker
    • Agile

  4. –

    DevOps Engineer

    Pharmaceutical industry · freelance

    Automation of large-scale data ingestion and integration workflows in a data ingest team. I integrated Informatica Big Data Management with an AWS-based big data cluster and automated infrastructure and deployments in a regulated environment.

    • AWS
    • Jenkins
    • Docker
    • Ansible
    • Python
    • Bash
    • Artifactory

  5. –

    DevOps Engineer

    Banking · freelance

    Continuous integration, build and release engineering on an internet banking project. Repository ownership and release management, PL/SQL development, technical support for the project infrastructure and production on-call duty.

    • Jenkins
    • TeamCity
    • Oracle
    • Docker
    • Maven
    • Gradle
    • Ansible
    • ELK
    • Git

  6. –

    Oracle DBA → Database Engineer → Senior Database Engineer

    Technology company

    From database and system administration to technical lead for the backend of a marketing campaign platform: Oracle administration (OLTP and data warehouse), data modelling, ETL and PL/SQL design and development. I led the development team through a major application upgrade with both versions running in parallel.

    • Oracle
    • PL/SQL
    • ETL
    • MS SQL
    • Linux
    • Nagios

  7. –

    Support Engineer

    Logistics

    Administration and application support for four global logistics applications: Oracle database support, WebLogic application server administration and analysis work.

    • Oracle
    • WebLogic

  8. –

    Enterprise System Administrator

    Public sector

    Design and administration of a Windows 2000 domain and Citrix terminal services for more than 1,500 users, including security policies. Administration and hardening of network services and application servers in a mixed Unix/Linux and Windows environment.

    • Windows Server
    • Citrix
    • Exchange
    • Linux

  9. –

    Hardware & Software Engineer, IT Consultant

    IT services

    System implementation and administration for customers, building and servicing PCs and servers, and teaching computer courses including preparation for ECDL certification.

    • Windows NT
    • Networking

Services

How I can help

Six areas that build on one another. We usually start with one and add others as the team’s real needs become clear.

  • DevOps Platform Engineering

    Design and operation of CI/CD, automation, infrastructure as code, container platforms and developer experience.

  • GitHub Enterprise & Developer Experience

    Migrations, governance, repository organisation, workflows, reporting and GitHub adoption in enterprise environments.

  • AI-Assisted Engineering

    Practical rollout of GitHub Copilot, AI coding assistants, agents and MCP. Including governance, secure integrations and measurement of real usage.

  • Software Supply-Chain Security

    GitHub Advanced Security, secret scanning, dependency management, SBOM, SonarQube, Artifactory and security governance.

  • Automation & Data Platforms

    Python, Bash, API integrations, workflow orchestration, data ingestion, PostgreSQL/SQLite, Grafana and Prometheus.

  • Cloud & Infrastructure

    Azure, AWS, OpenShift, Kubernetes, Docker, Terraform, Ansible and reliable deployment patterns.

Expertise

Technology constellation

The tools I work with, grouped by the problem they solve.

  • Platform & Cloud

    • OpenShift
    • Kubernetes
    • Docker
    • Podman
    • Terraform
    • Ansible
    • Azure
    • AWS

  • CI/CD & Automation

    • GitHub Actions
    • Jenkins
    • Git
    • Python
    • Bash
    • Maven
    • Gradle

  • GitHub & Developer Experience

    • GitHub Enterprise Cloud
    • Data Residency
    • Managed runners
    • Governance
    • Migration
    • Feature toggling

  • Security & Supply Chain

    • GitHub Advanced Security
    • SonarQube
    • Artifactory
    • Secret scanning
    • SBOM
    • Dependency management

  • AI & Agentic Workflows

    • GitHub Copilot
    • AI usage analytics
    • Custom AI models
    • Agents
    • MCP
    • Azure AI Foundry

  • Data & Observability

    • Data ingestion
    • ETL
    • Oracle
    • PL/SQL
    • PostgreSQL
    • SQLite
    • Grafana
    • Prometheus
    • ELK

Projects

Selected work

Anonymised case studies. No client names, and no figures I cannot back up.

GitHub Enterprise migration and platform governance

  • GitHub Enterprise
  • Migration
  • Governance
Context
A regulated enterprise environment with a large number of repositories and pipelines spread across several tools.
Challenge
Move repositories and workflows to GitHub Enterprise Cloud with Data Residency while teams keep delivering.
Approach
Migration in waves, consistent rules for organisations, permissions and branch protection, automation of repetitive steps.
Outcome
Unified development workflows and a clearer picture of repositories and their owners.

AI-assisted development adoption

  • GitHub Copilot
  • PoC
  • AI adoption
Context
Development teams wanted to try AI assistants; the organisation needed confidence about security and value.
Challenge
Validate GitHub Copilot in real work and set rules that satisfy security and compliance.
Approach
A proof of concept with selected teams, Copilot usage analytics, governance and secure integrations through MCP and custom models.
Outcome
AI as a governed, measurable part of the platform; decisions on further rollout rest on data.

DevOps platform and CI/CD

  • CI/CD
  • OpenShift
  • IaC
Context
Business-critical systems including internet banking on OpenShift; each team deployed in its own way, with many manual steps.
Challenge
Give teams a shared, reliable way to build and deploy without taking away their flexibility.
Approach
Reusable workflows and templates, infrastructure as code, standardised deployments to a container platform.
Outcome
Less manual work in deployments and faster developer onboarding.

Software supply-chain security

  • Secret scanning
  • SonarQube
  • SBOM
Context
A growing number of dependencies and repositories without a single view of security risk.
Challenge
Introduce checks that catch problems early without slowing developers down unnecessarily.
Approach
Secret scanning, dependency management through Artifactory, quality gates in SonarQube and SBOM generation in the pipeline.
Outcome
Better visibility of repositories and security risks; checks run automatically on every change.

Data ingestion and observability

  • Python
  • PostgreSQL
  • Grafana
Context
Large-scale data ingestion in a regulated environment and operational data from several systems, partly processed by hand.
Challenge
Automate data intake and be sure processing succeeded – or know why it did not.
Approach
Automation in Python, Bash and Ansible, workflow orchestration, integration with a cloud big data cluster, dashboards and alerts in Grafana and Prometheus.
Outcome
Reliable automated data intake and operational visibility without manual digging.

Approach

How we work together

Six steps from the first conversation to long-term improvement. The depth of each depends on the size of the engagement.

  1. Discovery

    Understanding the context, teams, technologies and goals.

  2. Assessment

    Analysis of the current state, risks and opportunities.

  3. Design

    A practical and secure solution design.

  4. Implementation

    Implementation, automation and integration.

  5. Enablement

    Documentation, training and adoption support.

  6. Continuous improvement

    Measurement, iteration and long-term improvement.

Contact

Let’s talk it through

Working on a platform, GitHub Enterprise or bringing AI into development? Send me a few lines about where you are and where you want to get to. I will get back to you.

Book a consultation

This site has no form and no tracking. Your message goes straight to my inbox.