Mexico

Reports To: Director of Cloud Infrastructure

Role Overview:
Join our Core Kubernetes Operator Development team, where we’re pushing the boundaries of Kubernetes innovation. As a Kubernetes Controller Developer (Golang), you will play a crucial role in building “01”, our cloud-agnostic Platform as a Service (PaaS), driven by full-fledged Kubernetes operators and agents.

This position requires a strong background in Kubernetes internals and Golang programming, particularly in developing and managing Kubernetes controllers. If you’re a proactive problem solver with experience in building cloud-native infrastructure, this is your opportunity to contribute to a transformative platform.

We highly encourage candidates with a solid programming foundation and a hunger to explore the cloud-native world to apply. Comprehensive onboarding and professional development support will be provided.

Key Responsibilities (Not limited to):

Collaborate in Agile teams, taking ownership of development stories with minimal supervision.
Partner with internal teams and clients to accurately capture technical requirements.
Design, build, deploy, and maintain Kubernetes controllers and operators using Golang.
Identify gaps in current systems and propose or implement technical improvements.
Apply best practices across the full software development lifecycle.
Create and execute unit, regression, and E2E tests for operator reliability.
Work in Linux environments and troubleshoot issues in containerized applications.
Contribute to CI/CD workflows for seamless testing and deployment.

Essential Skillset:

Kubernetes Controller Development: Proven expertise in building and maintaining controllers and operators.
Proficiency in Golang: 2+ years writing idiomatic, well-tested Go code for Kubernetes projects.
Deep understanding of Kubernetes APIs and libraries including client-go, CRDs, and API extensions.
Hands-on experience with:
- Kubebuilder – For scaffolding controllers and CRDs
- Operator SDK – For building Operators with OLM support
- controller-runtime – For abstracting Kubernetes client logic
Strong testing skills, including unit, load, and E2E tests for operators.
Familiarity with containerization (Docker) and orchestration (Kubernetes).
Comfortable working in Linux with debugging tools and CLI.
2+ years experience working with CI/CD tools like Jenkins, GitHub Actions, Tekton, or similar.

Preferred Skills (Nice to Have):

CKA or CKAD certifications.
Hands-on experience managing production-grade Kubernetes clusters.
Knowledge of Infrastructure as Code tools (e.g., Terraform).
Exposure to major cloud providers: AWS, GCP, or Azure.
Scripting experience in Shell or Python.

What We Offer:

A chance to build infrastructure automation tools that power real-world workloads.
Opportunity to work on bleeding-edge cloud-native technologies with a global impact.
Collaborative and innovation-driven culture, with strong engineering mentorship.
ARemote-friendly setup and flexible work culture.
Career development in one of the most in-demand areas of DevOps.

Reports To: Director of Cloud Infrastructure

About the Role.

You will be part of our Platform Engineering team that takes GitOps and 100% (Infra as Code) IaC seriously. You will be involved in development of a Cloud Agnostic Platform as a service (PaaS) product, we called it 01, that leverages Kubernetes. Our product aims to reduce significant time to roll out the Cloud Native application across the managed or on premise. This role requires Kubernetes experiences with strong programming skills.

Responsibilities:  

Work in an Agile team and be able to take ownership of stories with minimal direction
Work closely with our clients, understand and capture their requirements
Able to see gaps and areas of improvement in process as well as technologies, providing recommendations and taking the initiative to fix issues
Quick learner and able to adapt to new technologies and teams quickly
People who can interact well in both group and one-to-one set-up Experience with software development lifecycle.

Primary Skillset:

Experience in cloud and container solutions such as Docker and Kubernetes
Familiar with AWS, GCP or Azure
Experience in Linux Environment and debugging tools (2+ years)
Working experience in Linux script writing (Shell, Python etc) (2+ years)
Familiar with AWS, GCP or Azure (2+ years)
Familiar with at least one CI/CD tool: Jenkins, Gitlab CI (2+ years)
Experience in GOLANG (1+ years)

Secondary Skillset (Optional – nice to have):

Additional certification such as CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer) etc will be a bonus
Experience in containers, registries and microservices build using Springboot framework would be an added advantage
Experience in creating and managing production scale Kubernetes clusters
Experience with Terraform

Reports To : Director of Product Development

We are looking for a highly skilled AI Agent Engineer to develop autonomous AI agents that interact, reason, and adapt to dynamic environments. You will design intelligent systems that utilize machine learning models, natural language processing (NLP), and reinforcement learning techniques to create agents capable of reasoning, learning, and decision-making in real-time.

Responsibilities:
Agent Design: Develop architectures for autonomous AI agents, utilizing techniques like reinforcement learning (RL), multi-agent systems (MAS), and decision-making frameworks (e.g., Markov Decision Processes).

NLP & Dialogue Systems: Build conversational AI agents using state-of-the-art NLP techniques (e.g., transformers, BERT, GPT-4, T5) and frameworks like spaCy or Rasa.

ML Model Integration: Train, fine-tune, and optimize deep learning models (using TensorFlow, PyTorch, or Keras) for various agent tasks like perception, planning, and execution.

Autonomous Learning: Implement systems for continuous learning and adaptation using reinforcement learning (RL), supervised, and unsupervised learning methods.

AI Ethics: Incorporate fairness, interpretability, and transparency into AI agents, ensuring compliance with ethical AI principles.

Agent Integration: Design and implement systems to integrate AI agents into existing products, ensuring performance and reliability under production loads.

Evaluation: Set up testing and evaluation pipelines for measuring agent performance (accuracy, task completion rate, response time) and improvement.

Technical Skills:
Machine Learning: Deep understanding of reinforcement learning, deep learning (CNNs, RNNs), NLP, and computer vision techniques.

NLP Frameworks: Expertise in transformer models (e.g., GPT-3/4, BERT, T5), and frameworks such as spaCy, Hugging Face Transformers, AllenNLP, and OpenAI API.

Frameworks: Proficient in deep learning frameworks like TensorFlow, PyTorch, Keras, and OpenAI Gym for reinforcement learning.

Algorithms: Familiarity with search algorithms, decision trees, and Markov models for autonomous decision-making.

Programming: Advanced proficiency in Python, C++, and potentially Java for high-performance applications.

Cloud Services: Familiarity with cloud-based AI services (AWS SageMaker, Google AI Platform) for scalable model training and deployment.

Nice to Have:
Experience with multi-agent reinforcement learning (MARL).

Knowledge of graph neural networks or neural-symbolic systems.

Experience working with edge AI and deployment on IoT devices.

IT Consulting

DevSecOps

DevOps

Site Reliability Engineering

Cloud Advancement

Managed Kubernetes Services

Platform Engineering

Infrastructure Automation

Data Intelligence And Innovation

Cloud Native Architecture

Internet of Things (IoT)

SAP

DevEx

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