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About This Role
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.
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 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.
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.
SKILLS \& REQUIREMENTS
Qualifications, Skills, and Experience
- 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.
Preferred Skills
- 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.
EEO Statement
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 fairly, 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.
ABOUT THE COMPANY
In a world with an ever\-growing population with climate and sustainability challenges and with changing consumer demands, the global food industry is going through a profound transformation.
Did you know that 815 million people go to bed hungry every night. At the same time, 1/3 of all food being produced globally is wasted.
The Speria business is all about changing this. Not only to improve animal welfare, drive yield and sustainability for actors in the global food supply chain, but also to help to feed the world by enabling change in the way we farm and produce food.
Our contribution is to create a digital ecosystem including data capture platforms as connected controllers and IoT and sensors, that together with predictive AI and real\-time monitoring allow farmers and growers to improve animal welfare and maximize production while minimizing waste and Co2 emissions – ensuring a transparent and sustainable food production for a growing population.
Speria – spearheading digitalization by providing innovative solutions enabling the green transition.
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Michael Crouse \| Contact Person
Speria
Atlanta \| Hybrid
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Speria, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Speria AI Hiring
Speria has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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