Machine Learning Operations Engineer

• Atlanta
• Hybrid

Job Description

At Speria MTech our company mission is to increase yield in protein production to help feed

the growing world population without compromising animal welfare or damaging the planet.

We aim to create software that delivers real-time data to the entire supply chain that allows

producers to get better insight into what is happening on their farms and what they can do to

responsibly improve production.

Speria MTech is the industry-leading provider for Live Animal Protein Production Performance

Management Tools. For over 30 years Speria MTech has provided cutting-edge enterprise

data solutions for all aspects of the live poultry operations cycle. We provide our customers

with solutions in Business Intelligence Live Production Accounting Production Planning and

Remote Data Managementall through an integrated system. Our applications can

currently be found running businesses on six continents in over 50 countries. Speria MTech

has built an international reputation for equipping our customers with the power to utilize

comprehensive data to maximize profitability.

With over 300 employees globally Speria MTech currently has main offices in Mexico United

States and Brazil with additional resources in key markets around the world. Speria MTechs

headquarters is based in Atlanta Georgia and has approximately 90 team members in a

casual collaborative environment. Our work culture here is based on a passion for helping

our clients feed the world resulting in a flexible and rewarding atmosphere. We pride

ourselves for having a working atmosphere that encourages collaboration exceptional

development tooling training and ongoing opportunities to work with senior and executive

management.

Job Summary

We are seeking a highly skilled and motivated Machine Learning Operations (MLOps)

Engineer to join our dynamic team at Speria MTech. The ideal candidate will play a crucial

role in operationalizing machine learning and optimization systems by building

and maintaining the infrastructure deployment workflows and platform

capabilities required to run Applied AI solutions reliably in production.

This role focuses on model deployment scalable serving orchestration monitoring and

lifecycle management across Sperias integrated platforms. The MLOps Engineer works

closely with Machine Learning Engineers and Data Engineers to ensure that models and

decisioning systems are production-ready observable cost-efficient and seamlessly

integrated into downstream applications and workflows.

The role also helps improve platform performance and system efficiency by standardizing

deployment patterns reducing operational complexity and optimizing how machine learning

services are exposed and consumed across the organization.

We seek a solution-oriented individual who can provide answers rather than just identify

problems. Embracing continuous change is key as innovation and improvement are integral

to Speria MTechs culture. This person should have a service-minded attitude demonstrating

a passion for enhancing the work of others and simplifying processes for stakeholders.

Essential Functions & Responsibilities

Essential responsibilities include and functions of the Machine Learning Operations Engineer

are:

Build and maintain deployment pipelines for machine learning and optimization

services across development testing and production environments.

Design and operate scalable model serving patterns including APIs batch jobs and

scheduled workflows that expose machine learning capabilities to downstream

systems.

Manage model lifecycle workflows including model packaging versioning promotion

rollback and deployment automation.

Implement and maintain platform capabilities for observability monitoring and

alerting across model services and related production workflows.

Optimize model-serving systems for performance scalability reliability and cost

efficiency in cloud environments.

Collaborate with Machine Learning Engineers

to productionize models decisioning systems and intelligent workflows.

Work with Data Engineers to ensure production services have reliable access to

required data inputs feature outputs and supporting data pipelines.

Standardize deployment practices tooling and operational patterns to reduce

operational complexity and improve consistency across Applied AI systems.

Support orchestration of workflows that connect models and decisioning systems to

downstream applications and operational processes.

Maintain documentation for deployment architectures platform workflows monitoring

standards and operational runbooks.