Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. 

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

About the role

You'll join one of several teams building the low-level systems behind CAST AI's multi-cloud automation platform. The work sits at the intersection of Kubernetes, cloud infrastructure, and Linux.

Depending on your strengths, you'll work in one of these areas:

• Cluster and node lifecycle - custom node bootstrapping across AWS, GCP, and Azure. Controllers and operators managing full node lifecycle. Bridging the gap between autoscaler decisions and running VMs - including working around limitations of managed Kubernetes platforms like EKS, GKE, and AKS.
• Cross-cloud provisioning - orchestrating infrastructure across regions and clouds. Workflow orchestration, provisioning integrations, networking and identity across cloud boundaries.
• Live container migration - zero-downtime relocation of running containers using CRIU checkpoint/restore, custom CRI-O and runc forks, and forked cloud CNI plugins. Memory page transfer, TCP session migration, process-level checkpoint/restore. Kernel-level Linux engineering applied to Kubernetes.
• Storage optimization - dynamic volume rightsizing, BtrFS compression, custom CSI drivers. Full storage stack from cloud disk APIs through filesystem internals to Kubernetes PV/PVC lifecycle.

Common across all teams: Go, deep Kubernetes, multi-cloud, building beyond what the ecosystem offers.

How we build

We invest heavily in agentic development and AI-powered tooling. Engineers work with code agents and automated workflows daily. We expect you to shape how these tools evolve.

Requirements:

• Strong backend fundamentals and production-grade coding, preferably Go.
• Hands-on Kubernetes internals, cloud infrastructure, or large-scale distributed systems experience.
• Clear English communication, collaborative and self-directed.
• Bias toward action and ownership.

What would make you stand out

• Deep knowledge of EKS, GKE, or AKS internals.
• Low-level Linux experience: process internals, networking (NAT, iptables, conntrack, SDN), filesystems, storage.
• Kubernetes or cloud-native OSS contributions.
• Cloud storage depth - block storage, CSI, volume management, filesystem internals.
• Container runtime experience (CRI-O, runc, containerd) or CRIU.
• Experience with AI coding agents and agentic development workflows.
• Comfort building where there's no playbook.

Responsibilities:

• Design, build, and operate backend services, Kubernetes operators, and controllers in Go across AWS, GCP, and Azure.
• Solve infrastructure-layer problems — networking, scheduling, storage, compute, or container runtime internals.
• Build and maintain high-throughput services (gRPC/REST) and async orchestration workflows.
• Shape public and internal APIs.
• Leverage and improve AI-powered development workflows.
• Participate in design reviews, code reviews, on-call, and customer deep-dives.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• Technical interview (system design)
• Live coding
• Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

Why Kimchi?

Kimchi is the AI platform inside CAST AI. We started by helping companies run LLMs on their own Kubernetes clusters - now we're building the execution layer where agents do real work.

Our Infrastructure today: multi-model inference (MiniMax, Kimi, GLM-5, Nemotron, DeepSeek) with intelligent routing, an OpenAI-compatible API, and deployment flexibility from our GPUs to your VPC. The inference layer is the foundation. What we're hiring for sits on top of it: coding agents, agent runtimes, orchestration systems, and the reliability engineering that makes them actually finish things.

Tech Stack: TypeScript, Go, Kubernetes, AWS/GCP/Azure, MCP, Prometheus/Grafana/Loki, GitLab CI, ArgoCD.

Why harness engineering matters here
OpenAI and Anthropic ship models. They also ship one harness each - the scaffolding that turns a raw model into something that can plan, execute, recover, and complete work. We ship a different kind of harness: one built for cost-conscious, long-horizon autonomy, running on inference infrastructure we control end-to-end.
A decent model with a great harness beats a great model with a bad harness. We've watched this play out. The gap between what today's models can do and what you see them doing is largely a harness gap - and that gap is where we operate.

What you'll build
The ratchet.
Every time our agent makes a mistake, we engineer a solution so it never makes that mistake again. That means hooks that enforce constraints the model "knows" but forgets: pre-commit lint checks, permission gates, context compaction before the window fills. Success is silent, failures are verbose.

Long-horizon execution.
Our harness is built around spec-driven autonomy: meta-prompting, fresh context per task, worktree-per-slice git strategy, automatic replanning, crash recovery, stuck detection. We're implementing Ralph loops - when the model tries to exit, we intercept and reinject the goal into a fresh context. The agent reads state from disk and continues. Multi-session, multi-day work, without context rot.

Planner/executor splits.
Planning with a reasoning model, executing with a fast one, evaluating with a third. Separating generation from evaluation beats self-verification because agents reliably skew positive when grading their own work.

The harness surface.
CLI, TUI, MCP integration, sandboxed execution, telemetry. Our AGENTS.md is short - every line traces to a specific thing that went wrong. TypeScript on the surface, Go where it matters.

Memory and context.
Moving agents off laptops, giving them state that survives across sessions, managing context so information lands where it's actionable. Compaction, tool-call offloading, progressive skill disclosure.

What makes this different (with receipts)
You've seen the pitch: "we route to the best model." Everyone says that. Here's what we actually have:

• GPU infrastructure we own. Not just an API reseller. From GPU placement across clouds to the inference endpoint your agent calls - we control the cost curve.
• A harness-first thesis. We treat agent failures as configuration problems, not model problems. When we moved from a stock harness to our own, completion rates on internal benchmarks improved by 40%+ on the same model.
• Agents.md that earns every line. No brainstormed rules - every constraint in our system prompt traces to a real failure we saw and fixed.

Requirements:

• You've used AI coding agents in anger. Not demos - real work. You have opinions about Claude Code, Codex, OpenCode, Cursor. You know what it feels like when an agent gets stuck and why.
• Strong TypeScript or Go in production. Comfort moving between them. Our surface is TypeScript; our core is Go.
• You think in harness terms. You read "the agent hallucinated" and your first instinct is to ask what context it was missing, what hook should have caught it, what constraint should exist.
• You drive features end-to-end. Design → build → ship → measure → iterate. We don't have layers that absorb ambiguity for you.

Responsibilities:

• Build and evolve the agent harness - ship hooks, permission gates, and context compaction. Every AGENTS.md constraint traces to a failure you personally diagnosed.
• Own long-horizon execution - multi-session task completion via spec-driven prompting, worktree-per-slice git, Ralph loop recovery, and stuck detection. Completion rate is your metric.
• Architect planner/executor/evaluator pipelines - planning with a reasoning model, execution with a fast one, evaluation with a third. No self-verification.
• Manage agent memory and context - state persistence across sessions, context compaction, tool-call offloading. Zero context rot on multi-day work.
• Own the harness surface - CLI, TUI, MCP integrations, sandboxed execution, telemetry. TypeScript on the surface, Go where it matters.

What success looks like (after 6 months):

• You've shipped at least one major harness feature end-to-end: designed it, built it, measured it, iterated.
• You've added constraints to our AGENTS.md based on failures you personally observed and diagnosed.
• You've improved a measurable reliability metric - completion rate, context efficiency, or cost per successful task.
• You've formed strong opinions about where our harness is load-bearing and where it's dead weight.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience).
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. 

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

We are hiring across multiple teams!

As a Senior Software Engineer, you will have the opportunity to work on different key features of our product. We are currently hiring Senior Software Engineers for the following teams:

- Workload Optimization - Automates workload resource management by dynamically adjusting resource allocations, helping developers significantly reduce costs and improve application reliability.

- Karpenter - The Karpenter team powers the integration between Karpenter and Cast AI, bringing enterprise capabilities to the most popular open source Kubernetes autoscaler. We enhance Karpenter with advanced features that improve application reliability and performance while optimizing costs. By joining the team, you’ll bridge open source innovation with enterprise requirements, directly impacting how organizations run Karpenter at scale.

- Reporting - Builds a scalable reporting system that ingests millions of rows per second into our time-series databases, providing insights into cost savings, workload efficiencies, and Cast AI automation impact.

- Pricing - Drives the synchronization of public and customer cloud resources, availability, and dynamic pricing across all major cloud providers. Empowers autoscaling by leveraging discounts, commitments, and cross-cluster tracking to maximize savings. Provides a reliable source of truth for node pricing, resources, components, discounts, and commitments.

- Autoscaler - Automates Kubernetes node autoscaling to optimize clusters, balance workloads, remove underutilized nodes, and dynamically allocate capacity in real-time, thereby reducing cluster costs by half.

- Identity -  Builds and maintains the trust and access foundation for the entire platform, ensuring every user, service, and workload authenticates and interacts securely and seamlessly at scale.

- Billy -  Powers the critical day-2 operations layer of the platform - from billing and audit trails to notifications and feature flags - ensuring the platform runs reliably, transparently, and at scale for every customer, every day.

Here are some of the tools we use daily:

• Programming Languages: Go
• Cloud & Orchestration: Kubernetes, AWS, GCP, Azure
• Databases & Storage: PostgreSQL, Cloud Object Storage
• Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs
• Observability: Prometheus, Grafana, Loki, Tempo
• CI/CD & GitOps: GitLab CI with ArgoCD.

Requirements:

• Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.
• Deep hands-on experience with cloud platforms (AWS, GCP, or Azure) - including real understanding of how compute, networking, and storage work under the hood.
• Understanding of Kubernetes internals - autoscaling and networking.
• You've personally driven a complex project end-to-end.
• Strong debugging, optimization, and performance-tuning skills - including query profiling, index design, and database performance tuning beyond ORM usage.
• You've run observability tooling (Prometheus, Grafana, OpenTelemetry) in production.
• CI/CD and DevOps practices experience.
• Strong English skills, both verbal and written.
• Startup mindset: adaptable, proactive, and comfortable with ambiguity.

Responsibilities:

• Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale.
• Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management.
• Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1-4 weeks).
• Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services.
• Collaborate with product and other engineering teams to solve problems that don't have textbook solutions.
• Work with time-series data, cloud provider APIs, and Kubernetes control plane internals.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• Technical interview (system design)
• Live coding
• Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. 

For candidates at the P3 / Senior SRE level, starting monthly compensation begins at 3 800 € gross, with the final offer tailored for each candidate based on their skills and experience. Stock options and a comprehensive benefits package are also included. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1

We want you to join us full-time as a Senior Software Engineer in our Data Pipeline team. We work remotely first (from Central & Eastern Europe), but we are more than happy to meet you in our nice offices in Bratislava, Brno or Prague. And if you are interested in who will be your engineering manager, check out Vaclav's Linkedin. Salary range starting from 3700 EUR gross per month, going up depending on your experience and skills.

Intrigued? Read on 🙂…

Your responsibilities

• You will develop a data pipeline processing a large amount of data reliably and at a high rate using Python, Go, MongoDB, GCP and much more.
• For example, our clients feed their visitors’ behavior through real-time tracking to our platform. The data then can be analyzed and used for marketing automation. We process tens of thousands of requests per second 🚀. You may help improve the efficiency of our workers, improve monitoring or help with autoscaling of our infrastructure.
• Imports are critical for our clients to utilize our platform to the fullest. You may help us to improve the throughput and reliability of our imports as we need to import millions of rows of data or help us implement integration with another data storage.
• We also are responsible for exporting data from our platform to Google’s BigQuery using google’s DataFlows, PySpark and Apache Beam allowing data access by our clients
• You will help us run and support our services in production handling high-volume traffic using Google Cloud Platform and Kubernetes.
• You will review the code of your peers and they'll review yours. We have high code quality standards and the four-eyes principle is a must!
• You will start on easier, self-contained projects and once you feel at home, you can move to real beasts. Challenging, complex projects that will leave a mark - we have plenty of those 💪.
• Later you might help us by participating in on-call rotation keeping our services up and running 🚀.

Your qualifications

• You have experience with Python and a solid grasp of engineering practices 🔧.
• If you have an experience with Go, that's a big advantage 💪.
• You have experience with ETL pipelines, ingest on a scale, starting from various connectors to SQL and non-SQL sources, followed by data cleanup, sanitization and transformations resulting in loading cleaned-up data into optimise storages for a given use-case
• You know how to behave in a remote-first environment 
• You are able to learn and adapt. It'll be handy while exploring new tech, navigating our not-so-small code base, or when iterating on our team processes.

Our tech stack

• Python, GO
• Apache Kafka, Kubernetes, GitLab
• Google Cloud Platform, DataFlow, PySpark, Apache Beam, BigQuery, BigLake Table
• DataProc, PySpark, Flink, 
• Mongo, Redis
• … and much more 🙂

Your success story

• During the first 30 days, you will get to know the team, the company, and the most important processes. You’ll work on your first tasks. We will help you to get familiar with our codebase and our product.
• During the first 90 days, you will participate in your first, more complex projects. You will help the team to find solutions to various problems, break the solution down into smaller tasks and participate in implementation. You will learn how we identify problems, how we prioritize our efforts, and how we deliver value to our customers.
• During the first 180 days, you’ll become an integral part of the team. You will achieve the first goals we will set together to help you grow and explore new and interesting things. You will help us to deliver multi-milestone projects bringing great value to our customers. You will help us mitigate your first incidents and eventually even join the on-call rotation. You will get a sense of where the team is heading and you’ll help us to shape our future.
• Finally, you’ll find out that our values are truly lived by us 💛. We are dreamers and builders. Join us!

#LI-KP1

The Data Pipeline team is a backend-focused engineering team that is built on strong DevOps principles. We believe in autonomy, and we trust data. As the team grows we need to support it by onboarding another DevOps/SRE to pair with our existing one to form an effective duo helping the team to accelerate. We work remotely first, but we are more than happy to meet you in our nice office in Bratislava, Brno or Prague. Salary range starting from 4 200 EUR gross per month, going up depending on your experience and skills. And if you are interested in who will be your engineering manager, check out Vaclav's Linkedin.

Intrigued? Read on 🙂…

Your responsibilities

• Your task is to build and maintain an ecosystem where engineers can safely and efficiently develop, debug and operate their services running in GCP, Kubernetes using DataFlow, DataProc and Python with Go
• You make sure the services have high level of observability, enabling us to provide quality service for our customers
• Further services can scale vertically and horizontally based on current load, operational and telemetric data (OTEL, Prometheus, Victoria Metrics) 
• Team have enough insights about health of our services (Grafana, Alerting, PageDuty)
• You helps the team to fulfill security requirements given ISO and SOC2 audits, by enforce security principles like key distribution, key rotation, authorisation & authentication on service level, data encryption at transit, data isolation, resource limitations, quality of service, audit logs (mainly by Enovy proxies)
• You contribute to our tooling, so we have tools in place for debugging, troubleshoot and performance testing
• You automate manual/semi-manual steps deployment and instance setup
• You have hands on on L3 support and incident resolutions
• CI pipelines have linters, security scans, code smell detection enabling engineers to produce quality MRs

Your qualifications

Impact: 

• You can articulate how your contributions have transformed the way engineers work and think by fostering a strong DevOps/SRE culture.
• You can demonstrate how impactful your work as an SRE or DevOps Engineer can be in connection to business success

Ownership: 

• You understand the importance of you build - you run it principle and you love the feeling you own it
• You are mindful of the costs associated with running our service, which translates into effective vertical and horizontal pod autoscaling and detailed telemetry insights.

Systematic approach: 

• You believe the infrastructure as a code is the only thing that can bring stability into chaos 
• Terraform is your daily bread, and HELM deployments are your second-best friend

Data-driven:

• You use telemetry data and metrics to provide feedback to engineers on how the application and services behave
• You can navigate yourself in complex service architecture by using distributed debugging

Technical skills: 

• You have experience with Python and a solid grasp of engineering practices
• A big advantage is, if you have an experience with Go, or with ETL pipelines

You don’t hesitate to participate in OnCall rotation 24/7 support

You know how to behave in a remote-first environment 

You are able to learn and adapt. It'll be handy while exploring new tech, navigating our not-so-small code base, or when iterating on our team processes.

Our tech stack

• Python, GO
• Apache Kafka, Kubernetes, GitLab
• Google Cloud Platform, BigQuery, BigLake Table
• Open formats IceBerg, Avro, Parquet
• DataFlow, Apache Beam, DataProc, Spark, 
• Mongo, Redis
• … and much more 🙂

Your success story.

• During the first 30 days, you will get to know the team, the company, and the most important processes. You’ll work on your first tasks. We will help you to get familiar with our codebase and our product.
• During the first 90 days, you will start contributing to team’s L3 rotation, troubleshooting, debugging that should let you better understand what is what and perhaps also to come with fresh ideas on how to improve our services and monitoring.
• During the first 180 days, you’ll become an integral part of the team by actively contributing to the team's projects as well as to onCall rotation. 

Finally, you’ll find out that our values are truly lived by us 💛. We are dreamers and builders. Join us!

#LI-KP1

We want you to join us full-time as a Senior Software Engineer in our Data Pipeline team. We work remotely first (from Central & Eastern Europe), but we are more than happy to meet you in our nice offices in Bratislava, Brno or Prague. And if you are interested in who will be your engineering manager, check out Vaclav's Linkedin. 

Intrigued? Read on 🙂…

Your responsibilities

• You will develop a data pipeline processing a large amount of data reliably and at a high rate using Python, Go, MongoDB, GCP and much more.
• For example, our clients feed their visitors’ behavior through real-time tracking to our platform. The data then can be analyzed and used for marketing automation. We process tens of thousands of requests per second 🚀. You may help improve the efficiency of our workers, improve monitoring or help with autoscaling of our infrastructure.
• Imports are critical for our clients to utilize our platform to the fullest. You may help us to improve the throughput and reliability of our imports as we need to import millions of rows of data or help us implement integration with another data storage.
• We also are responsible for exporting data from our platform to Google’s BigQuery using google’s DataFlows, PySpark and Apache Beam allowing data access by our clients
• You will help us run and support our services in production handling high-volume traffic using Google Cloud Platform and Kubernetes.
• You will review the code of your peers and they'll review yours. We have high code quality standards and the four-eyes principle is a must!
• You will start on easier, self-contained projects and once you feel at home, you can move to real beasts. Challenging, complex projects that will leave a mark - we have plenty of those 💪.
• Later you might help us by participating in on-call rotation keeping our services up and running 🚀.

Your qualifications

• You have experience with Python and a solid grasp of engineering practices 🔧.
• If you have an experience with Go, that's a big advantage 💪.
• You have experience with ETL pipelines, ingest on a scale, starting from various connectors to SQL and non-SQL sources, followed by data cleanup, sanitization and transformations resulting in loading cleaned-up data into optimise storages for a given use-case
• You know how to behave in a remote-first environment 
• You are able to learn and adapt. It'll be handy while exploring new tech, navigating our not-so-small code base, or when iterating on our team processes.

Our tech stack

• Python, GO
• Apache Kafka, Kubernetes, GitLab
• Google Cloud Platform, DataFlow, PySpark, Apache Beam, BigQuery, BigLake Table
• DataProc, PySpark, Flink, 
• Mongo, Redis
• … and much more 🙂

Your success story

• During the first 30 days, you will get to know the team, the company, and the most important processes. You’ll work on your first tasks. We will help you to get familiar with our codebase and our product.
• During the first 90 days, you will participate in your first, more complex projects. You will help the team to find solutions to various problems, break the solution down into smaller tasks and participate in implementation. You will learn how we identify problems, how we prioritize our efforts, and how we deliver value to our customers.
• During the first 180 days, you’ll become an integral part of the team. You will achieve the first goals we will set together to help you grow and explore new and interesting things. You will help us to deliver multi-milestone projects bringing great value to our customers. You will help us mitigate your first incidents and eventually even join the on-call rotation. You will get a sense of where the team is heading and you’ll help us to shape our future.
• Finally, you’ll find out that our values are truly lived by us 💛. We are dreamers and builders. Join us!

#LI-KP1

The Data Pipeline team is a backend-focused engineering team that is built on strong DevOps principles. We believe in autonomy, and we trust data. As the team grows we need to support it by onboarding another DevOps/SRE to pair with our existing one to form an effective duo helping the team to accelerate. We work remotely first, but we are more than happy to meet you in our nice office in Bratislava, Brno or Prague. And if you are interested in who will be your engineering manager, check out Vaclav's Linkedin.

Intrigued? Read on 🙂…

Your responsibilities

• Your task is to build and maintain an ecosystem where engineers can safely and efficiently develop, debug and operate their services running in GCP, Kubernetes using DataFlow, DataProc and Python with Go
• You make sure the services have high level of observability, enabling us to provide quality service for our customers
• Further services can scale vertically and horizontally based on current load, operational and telemetric data (OTEL, Prometheus, Victoria Metrics) 
• Team have enough insights about health of our services (Grafana, Alerting, PageDuty)
• You helps the team to fulfill security requirements given ISO and SOC2 audits, by enforce security principles like key distribution, key rotation, authorisation & authentication on service level, data encryption at transit, data isolation, resource limitations, quality of service, audit logs (mainly by Enovy proxies)
• You contribute to our tooling, so we have tools in place for debugging, troubleshoot and performance testing
• You automate manual/semi-manual steps deployment and instance setup
• You have hands on on L3 support and incident resolutions
• CI pipelines have linters, security scans, code smell detection enabling engineers to produce quality MRs

Your qualifications

Impact: 

• You can articulate how your contributions have transformed the way engineers work and think by fostering a strong DevOps/SRE culture.
• You can demonstrate how impactful your work as an SRE or DevOps Engineer can be in connection to business success

Ownership: 

• You understand the importance of you build - you run it principle and you love the feeling you own it
• You are mindful of the costs associated with running our service, which translates into effective vertical and horizontal pod autoscaling and detailed telemetry insights.

Systematic approach: 

• You believe the infrastructure as a code is the only thing that can bring stability into chaos 
• Terraform is your daily bread, and HELM deployments are your second-best friend

Data-driven:

• You use telemetry data and metrics to provide feedback to engineers on how the application and services behave
• You can navigate yourself in complex service architecture by using distributed debugging

Technical skills: 

• You have experience with Python and a solid grasp of engineering practices
• A big advantage is, if you have an experience with Go, or with ETL pipelines

You don’t hesitate to participate in OnCall rotation 24/7 support

You know how to behave in a remote-first environment 

You are able to learn and adapt. It'll be handy while exploring new tech, navigating our not-so-small code base, or when iterating on our team processes.

Our tech stack

• Python, GO
• Apache Kafka, Kubernetes, GitLab
• Google Cloud Platform, BigQuery, BigLake Table
• Open formats IceBerg, Avro, Parquet
• DataFlow, Apache Beam, DataProc, Spark, 
• Mongo, Redis
• … and much more 🙂

Your success story.

• During the first 30 days, you will get to know the team, the company, and the most important processes. You’ll work on your first tasks. We will help you to get familiar with our codebase and our product.
• During the first 90 days, you will start contributing to team’s L3 rotation, troubleshooting, debugging that should let you better understand what is what and perhaps also to come with fresh ideas on how to improve our services and monitoring.
• During the first 180 days, you’ll become an integral part of the team by actively contributing to the team's projects as well as to onCall rotation. 

Finally, you’ll find out that our values are truly lived by us 💛. We are dreamers and builders. Join us!

#LI-KP1

Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. 

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

About the role

Databases are notoriously hard to optimize—with 350+ configuration parameters in PostgreSQL alone, even experienced engineers struggle to find the right settings. Have you ever dreamed about a database that continuously self-optimizes itself in the background, so you can focus on what really matters: building great products?

Sometimes your queries slow down and it's not immediately clear why. Imagine a system that proactively detects performance degradation, analyzes the root cause, and automatically improves your queries and indexes for you—creating pull requests with the exact optimizations needed.

CAST AI is seeking a skilled senior software engineer with expertise in databases and interest in understanding the underlying technology that makes them work. Knowledge of implementation details around transactions, wire protocols, WAL, indexes, query planning, and other fundamental database features will enable you to build a product that automates what's traditionally been manual and complex.

You'll work on a greenfield project with high impact, creating intelligent systems that make database optimization effortless for our customers.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

Requirements:

• Experience working with and troubleshooting PostgreSQL and/or MySQL in production environments
• Experience troubleshooting database-related issues in one of your previous roles—whether as a DBA, backend engineer, SRE, or any position where you've debugged slow queries, optimized indexes, or resolved production database problems
• Strong programming skills in Go and/or C++
• Strong problem-solving skills and the ability to troubleshoot complex issues in a production environment
• Strong written and verbal communication skills in English
• Ability to work independently and collaboratively within a team
• Startup mindset: adaptable, proactive, and comfortable with ambiguity
• A proactive, problem-solving mindset with a "yes we can" attitude

Here are some of the tools we use daily:

• Mostly Golang with C++ in the most critical data pathways
• Extending Envoy with custom network filters
• ClickHouse and PostgreSQL for business metrics
• GRPC and REST APIs
• GitLab CI with ArgoCD as our GitOps CD engine
• Prometheus, Grafana, Loki, and Tempo for observability
• Deployment charts are written in Helm.

Responsibilities:

• Design, build, and operate database optimization services in Go and C++.
• Develop features for query analysis, index recommendations, and automated performance tuning that deliver measurable database cost savings.
• Extend and maintain integrations with PostgreSQL and MySQL protocols, cloud database services (RDS, Cloud SQL, Azure Database).
• Implement query parsing, plan analysis, and recommendation algorithms that identify optimization opportunities.
• Develop monitoring and alerting systems for database performance metrics, connection health, and cost tracking.
• Shape public and internal APIs with an eye for simplicity and developer experience.
• Participate in design reviews, code reviews, and occasional customer deep‑dives.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Private health insurance.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

#LI-Remote