Founding AI Platform Engineer (MLOps / Backend)

Jobgether • Brazil

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

This foundational role involves building and maintaining the AI platform infrastructure, enabling ML and GenAI capabilities to scale reliably in production. Key responsibilities include model lifecycle management, deployment, serving, experimentation, and CI/CD. Requires strong software engineering, backend services, cloud infrastructure, and MLOps experience, with proficiency in Python.

Key Highlights

Foundational engineering opportunity to shape AI platform systems.

Broad ownership across model lifecycle, deployment, serving, and observability.

Build production infrastructure for scalable, performant, and cost-efficient AI systems.

Key Responsibilities

Build and maintain infrastructure and tooling for training, evaluating, deploying, serving, and monitoring ML models and GenAI services.

Develop and operate production backend services, APIs, and pipelines supporting recommendations, agent workflows, and customer-facing integrations.

Improve CI/CD pipelines, automated testing, release processes, rollback strategies, and environment management.

Establish comprehensive observability across application health, model behavior, agent quality, latency, costs, and operational failure modes.

Build reproducibility and lifecycle management practices for models, prompts, datasets, configurations, and software releases.

Support experimentation and measurement infrastructure that enables ML and product teams to evaluate changes reliably.

Strengthen platform reliability, scalability, security, performance, and cost efficiency across the technology stack.

Troubleshoot production issues end-to-end and convert recurring operational problems into long-term engineering improvements.

Collaborate closely with ML, product, and engineering teams to move ambiguous initiatives from concept to completion.

Establish engineering standards and platform practices that can support future growth and increasing system complexity.

Identify platform, reliability, and scaling risks early and proactively address them before they affect customers or delivery.

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Technical Skills Required

Python Backend Services Cloud Infrastructure

Benefits & Perks

Fully remote work environment

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Nice to Have

Experience with MLOps workflows covering model training, evaluation, deployment, and monitoring is highly advantageous.

Experience serving machine-learning models or LLM-powered applications in production is a strong plus.

Familiarity with experimentation platforms, event pipelines, analytics instrumentation, or feature delivery platforms is beneficial.

Experience with agent evaluation, prompt versioning, retrieval and search infrastructure, or vector-backed systems is an advantage.

Experience supporting customer-facing APIs or SaaS platform infrastructure is preferred.

Job Description

This is a foundational engineering opportunity for someone who wants to shape the systems that turn ML and GenAI capabilities into reliable, production-ready products.You will operate at the intersection of backend engineering, cloud infrastructure, MLOps, platform reliability, and product delivery.The role offers broad ownership across model lifecycle management, deployment, serving, experimentation, observability, and CI/CD.You will build the production infrastructure that enables AI systems to scale safely while remaining performant, maintainable, secure, and cost-efficient.

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