Why Verifone

For more than 30 years, Verifone has established a remarkable record of leadership in the electronic payment technology industry. Verifone is one of the leading electronic payment solutions brands and among the largest providers of electronic payment systems worldwide.

Verifone has a diverse, dynamic, and fast-paced work environment in which employees are focused on results and have opportunities to excel. We take pride in working with leading retailers, merchants, banks, and third-party partners to invent and deliver innovative payment solutions around the world. We strive for excellence in our products and services and are obsessed with customer happiness.

Across the globe, Verifone employees are leading the payments industry through experience, innovation, and an ambitious spirit. Whether it’s developing the next generation of secure payment systems or finding new ways to bring electronic payments to emerging markets, the Verifone team is dedicated to the success of our customers, partners, and investors. It is this passion for innovation that drives every Verifone employee toward personal and professional success.

Verifone is proudly an in-office work culture as we see immense benefits to career development and business results from our colleagues being physically co-located.

What’s Exciting About the Role

Verifone is seeking a Kafka DevOps Engineer to join our Platform Engineering team. This is an operations-first role with a strong emphasis on scripting, automation, and pipeline development. You’ll be hands-on with day-to-day Kafka operations, reliability, tuning, and high availability for payment gateway solutions that process billions of transactions annually on-prem and in AWS Cloud. Beyond keeping the lights on, you’ll play a key role in building the data pipelines—of which Kafka is a core component—that power Verifone’s new AI, machine learning, and analytics initiatives. You’ll also be part of an active effort to migrate Kafka and related services to Kubernetes, giving you hands-on experience with a meaningful infrastructure modernization project. The technology footprint is broad: Redis, MongoDB, PostgreSQL, MySQL, Snowflake, and more—so you’ll grow well beyond a single-technology niche.

Key Responsibilities

Kafka Operations & Reliability

• Manage and support Apache Kafka clusters (including MSK), Kafka Connect ecosystem, and KSQL for high-throughput, fault-tolerant messaging and event streaming pipelines.
• Monitor system health, set up alerts, and drive incident response and root cause analysis (RCA).
• Handle day-to-day operational tasks including offset management, lag monitoring, consumer group management, and cluster rebalancing.
• Perform routine cluster maintenance: upgrades, configuration changes, scaling, and health checks.

Scripting, Automation & DevOps

• Develop and manage automation scripts using Python and Shell scripting to reduce manual toil and improve operational efficiency.
• Design, implement, and maintain robust CI/CD pipelines for patching automation.
• Build and maintain Infrastructure as Code (IaC) using tools such as Terraform, Ansible, or similar.
• Containerize and orchestrate workloads using Docker and Kubernetes, with the opportunity to help migrate Kafka and related services to Kubernetes.

Data Pipeline Development

• Build and optimize end-to-end data pipelines, with Kafka as a core streaming component alongside batch and ETL processes, enabling new AI, ML, and analytics use cases.
• Collaborate with development teams to implement best practices for data flow, security, scalability, and performance.
• Continuously identify and implement improvements to the software development lifecycle (SDLC).

Cloud & Infrastructure

• Manage cloud infrastructure on AWS (VMs, networking, storage, IAM, etc.).
• Support cloud migration tasks and assist in moving workloads between on-prem and cloud environments.

Required Qualifications / Skills

• 4+ years of overall engineering experience with 2+ years of hands-on experience supporting Kafka infrastructure in a large-scale production environment.
• Kafka Producer/Consumer microservices concepts and Kafka distributed architecture.
• Solid Linux fundamentals: networking basics, logs, system troubleshooting, process/memory, disk.
• Strong scripting and automation skills (Python, Bash) with a track record of reducing operational toil.
• CI/CD pipeline development and Infrastructure-as-Code experience (Terraform preferred).
• Cloud engineering skills, preferably AWS (EC2, VPC, IAM, MSK/ElastiCache, CloudWatch).
• Familiarity with observability tools (metrics/logs/tracing concepts) and incident response practices.
• Basic understanding of distributed systems tradeoffs (availability, consistency, partitions, backpressure).
• Strong communication and presentation skills with emphasis on executive communication.
• Flexible with regards to working shifts; on-call and weekends.

Preferred Skills (Highly Desired)

Data Engineering & Pipelines

• Data engineering skills including data analytics, data processing, ETL, and data lake technologies (batch and streaming, file formats like Parquet, table formats like Iceberg/Delta/Hudi, basic orchestration).
• Experience with AWS data tools (Athena, Glue, Iceberg, Redshift, etc.).
• Exposure to Kafka Streams, Apache Flink, or similar stream processing frameworks.

Database & Caching Technologies

• Operate Redis deployments for caching, ephemeral state, queues/streams, and rate limiting use cases.
• Relational DB experience: PostgreSQL and/or MySQL (indexing basics, vacuum/analyze, query plans, replication fundamentals).
• MongoDB operational familiarity (replica sets, elections, oplog basics, backup/restore).

Infrastructure & Security

• Container/Kubernetes familiarity (deployments, stateful workloads, storage classes).
• On-prem experience (VMware/KVM, storage, networking).
• Security fundamentals: least privilege, secrets management, encryption-in-transit/at-rest concepts.
• Experience working with PCI (Payment Card Industry Data Security) standards.

What We Offer

• Direct impact on Verifone’s global payment infrastructure—your work keeps billions of transactions flowing reliably every year.
• Ground-floor involvement in building the data pipelines that will drive Verifone’s AI, machine learning, and analytics strategy.
• Hands-on participation in a Kafka-to-Kubernetes migration—a resume-building modernization project from day one.
• Multi-technology exposure—Kafka, Redis, MongoDB, PostgreSQL, MySQL, Snowflake—so you grow as a well-rounded platform engineer, not a single-tool specialist.
• Mentorship from senior engineers and Kafka architects with deep production experience.
• Clear career progression path from mid-level to senior and lead engineer.
• Funded certification paths—Confluent (CCDAK, Certified Administrator), AWS (Solutions Architect, MSK Specialty), CKA—plus budget for courses and technical conferences.
• Hybrid/remote working flexibility with a collaborative team culture.
• Competitive compensation with performance-based incentives.

Our Commitment

Verifone is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Verifone is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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.

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