Embedded Machine Learning Engineer Jobs in Atlanta, GA

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 Management—all 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 MTech’s

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.

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 Speria’s 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 MTech's culture. This person should have a service-minded attitude, demonstrating

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

• 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.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
• 2–4 years of experience in software engineering, data engineering, MLOps, or platform engineering roles.
• Experience building and maintaining production systems, including deployment pipelines or distributed systems.
• Experience working with cloud-based environments for deploying and operating data or machine learning systems.
• Strong programming skills in Python and experience with scripting and automation for deployment workflows.
• Experience working with machine learning lifecycle tools and platforms (e.g., MLflow or similar).
• Experience designing and managing CI/CD pipelines and deployment workflows for machine learning systems.
• Experience with Databricks or similar platforms for machine learning lifecycle management, including model tracking, governance, and serving, is highly desirable.
• Experience implementing monitoring, logging, and observability for production systems.
• Strong understanding of system performance optimization, scalability, and cost efficiency.

• Familiarity with cloud-native data and compute services (e.g., serverless compute, managed databases, container platforms) is a plus.
• Experience working with containerization technologies (e.g., Docker, container platforms, or similar).
• Familiarity with deploying and managing containerized applications in cloud environments.
• Experience working with CI/CD pipelines, automation, or infrastructure-as-code tools.
• Experience supporting or operating machine learning systems in production environments.
• Familiarity with API development and model serving patterns (REST APIs, batch inference workflows).
• Ability to collaborate effectively with machine learning, data engineering, and platform teams.
• Familiarity with machine learning workflows and lifecycle processes, including model deployment, monitoring, and retraining.

Integrated into our shared values is Speria MTech’s commitment to diversity and equal

employment opportunity. All qualified applicants will receive consideration for employment

without regard to sex, age, race, color, creed, religion, national origin, disability, sexual

orientation, gender identity, veteran status, military service, genetic information, or any other

characteristic or conduct protected by law. Speria MTech is committed to being a globally

inclusive company where all people are treated fair, recognized for their individuality,

promoted based on performance, and encouraged to strive to reach their full potential. We

believe in understanding and respecting differences among all people. Every individual at

Speria MTech has an ongoing responsibility to respect and support a globally diverse

environment.