AI Software & Platform Engineer

AI Software & Platform Engineer

Location: Dublin, Ireland (Remote)

Experience: 4 to 8 Years

About LatentView

LatentView Analytics is a global data analytics and AI solutions company that partners with Fortune 500 and high-growth clients to turn data into competitive advantage. Our teams combine deep technical expertise with business context to ship measurable outcomes — not just models and dashboards — across industries.

Role Overview

We are looking for an AI Software & Platform Engineer to help build, deploy, operate, and integrate AI-powered applications and services.

The ideal candidate is a strong software/platform engineer with significant hands-on experience in Python, Kubernetes, Docker, backend systems, and production environments. The role sits at the intersection of software engineering, platform engineering, AI infrastructure, and data engineering. We are therefore looking for engineers with strong technical fundamentals who can learn new technologies quickly and adapt their skills as project requirements evolve.

Key Responsibilities

• Build and maintain backend services supporting AI-powered applications.
• Develop APIs and integrations between applications, services, and data systems.
• Containerize and deploy applications using Docker and Kubernetes.
• Develop and maintain CI/CD workflows.
• Support application deployment, monitoring, reliability, and troubleshooting.
• Work with engineering teams to improve scalability, reliability, and operational efficiency.
• Integrate AI agents and LLM-powered applications with backend services and data sources.
• Develop and maintain data integrations and pipelines as project needs evolve.
• Troubleshoot issues across application, infrastructure, and data layers.
• Contribute to engineering standards, automation, and operational best practices.
• Learn new technologies, frameworks, and platforms as required.

Required Skills

Software Engineering

·         4–10 years of professional engineering experience.

·         Strong hands-on experience with Python.

·         Experience developing backend services and APIs.

·         Strong understanding of software engineering fundamentals.

·         Experience with:

oGit

oTesting

oDebugging

oAPI development

oProduction software

oSystem integration

Platform Engineering

·         Strong hands-on Kubernetes experience.

·         Strong experience with Docker and containerized applications.

·         Experience deploying and operating production applications.

·         Good understanding of:

oKubernetes workloads

oDeployments

oServices

oConfiguration

oResource management

oTroubleshooting

·         Experience with CI/CD pipelines.

·         Familiarity with monitoring, logging, and observability.

AI / Agent Exposure

Hands-on experience with or familiarity with:

·         LLM applications

·         AI agents

·         RAG

·         Tool/function calling

·         AI application deployment

Data Engineering Fundamentals

·         Strong SQL skills.

·         Experience working with structured and semi-structured data.

·         Understanding of ETL/ELT concepts.

·         Experience building, maintaining, or supporting data pipelines.

·         Understanding of databases and data integration.

Preferred Skills

·         Kubernetes at scale

·         Infrastructure as Code

·         Terraform or equivalent

·         Distributed systems

·         Microservices

·         Workflow orchestration

·         Cloud platforms

·         Data processing frameworks

·         Event-driven architectures

·         Streaming technologies

·         Observability and tracing

·         Security and IAM

·         AI/ML infrastructure

·         Agent orchestration frameworks

What We Value

Strong Learning Agility

The role will involve technologies and systems that candidates may not have previously encountered.

We are looking for engineers who can:

·         Quickly understand unfamiliar technical environments.

·         Learn new infrastructure and deployment technologies.

·         Transfer experience from existing platforms to new environments.

·         Work independently through technical documentation.

·         Troubleshoot complex production problems.

·         Develop new skills as the team's needs evolve.

·         Transition across software, platform, AI, and data engineering responsibilities when required.