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Mlops Data Engineer Jobs in Georgia (NOW HIRING)

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Google Data Specialist

Atlanta, GA · On-site

$70K - $196K/yr

Build data pipelines, ETL/ELT processes, and integrations using GCP services such as: BigQuery ... establish MLOps processes (monitoring, retraining, deployment). * Support prompt engineering ...

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 ...

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... ML, data engineering, platform, and security. * 2+ years of experience with treat testing ...

The Data & Analytics organization empowers AXIS to make informed, datadriven decisions by ... MLOps and AI platform knowledge: MLflow, feature stores, model lifecycle and deployment patterns.

Develop and optimize data pipelines, feature engineering workflows, and large-scale data processing solutions using Python, SQL, and Spark. * Implement and support MLOps practices, including model ...

Data & AI Platform Engineer

Duluth, GA

$105K - $126K/yr

This is an early-career engineering role focused on building, operating, and improving cloud data ... MLOps). "Armanino" is the brand name under which Armanino LLP and Armanino Advisory LLC ...

Data & AI Platform Engineer

Brunswick, GA · On-site

$103K - $124K/yr

This is an early-career engineering role focused on building, operating, and improving cloud data ... MLOps). "Armanino" is the brand name under which Armanino LLP and Armanino Advisory LLC ...

Data & AI Platform Engineer

Atlanta, GA · On-site

$110K - $132K/yr

This is an early-career engineering role focused on building, operating, and improving cloud data ... MLOps). "Armanino" is the brand name under which Armanino LLP and Armanino Advisory LLC ...

Senior Data Architect

Atlanta, GA · On-site

$140 - $190/hr

... DevOps teams to architect and maintain MLOps processes * develop and enforce enterprise‑wide standards for metadata management, data contracts, and semantic version control * ensure data and its ...

New

Senior Cloud Engineer

Atlanta, GA

$53.50 - $71.75/hr

Title and Summary Senior Cloud Engineer Overview: Interested in building the next generation cloud ... Experience supporting Machine Learning, MLOps, or AI-driven data platforms. AWS and/or Databricks ...

New

Fine-tuning, prompt engineering, and optimization strategies * Establish MLOps and LLMOps ... Data engineering tools (Spark, Kafka, Hadoop) * Containerization and orchestration (Docker ...

Lead Forward Deployed Engineer - AWS

Atlanta, GA · On-site

$98K - $129K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Senior Forward Deployed Engineer- AWS

Atlanta, GA · On-site

$100K - $138K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Lead Forward Deployed Engineer - AWS

Atlanta, GA · On-site

$98K - $129K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Showing results 41-60

Mlops Data Engineer information

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What are the key skills and qualifications needed to thrive as an MLOps data engineer?

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

What job categories do people searching Mlops Data Engineer jobs in Georgia look for?

The top searched job categories for Mlops Data Engineer jobs in Georgia are:

What cities in Georgia are hiring for Mlops Data Engineer jobs?

Cities in Georgia with the most Mlops Data Engineer job openings:

AI Engineer Consultant

Deloitte

Atlanta, GA • Hybrid

Full-time

Re-posted 24 days ago


Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

45th of 151 rated financial services


Job description

Position Summary

Our Deloitte Human Capital team transforms technology platforms, drives innovation, and helps make a significant impact on our clients' success. We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that power Human Capital AI products and analytics. You will work with an AI Data Engineer (data ingestion, curation, governance, platform foundations) and a Lead AI Solutions Architect (end-to-end solution architecture, integration patterns, non-functional requirements), partnering closely with product, data science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI solutions.

This role is hands-on and delivery-oriented: you will ship production pipelines and services that support model training, real-time inference, and LLM applications using Claude-, GPT/Codex-, and Gemini-class models, and more implemented with strong governance, observability, and cost/performance discipline.

Recruiting for this role ends on 08/30/2026.

Work you'll do

As an AI Engineer Consultant on the HC Forward team, you will design, build, and run the trusted, governed data + feature + retrieval layer used by AI/ML and GenAI solutions. You will deliver reproducible datasets and features, operationalize quality and lineage, and enable secure consumption patterns for both predictive ML and LLM-based experiences.

  • Partner with the Lead AI Solutions Architect and AI Data Engineer to translate Human Capital product needs into secure, scalable technical designs and delivered solutions (APIs, services, pipelines, containers/serverless) meeting availability, performance, and security expectations.
  • Build and operationalize LLM-enabled capabilities (e.g., copilots, HR knowledge assistants, summarization, policy Q&A) using Claude/GPT(Codex)/Gemini, including secure endpoints, tool/function calling, and reusable prompt/context patterns.
  • Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry.
  • Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills).

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to provide clear guidance to others

The team

HC Forward is a dedicated innovation partner accelerating the future of Human Capital by building market-aligned products, platforms, and services that apply AI, data, and engineering to modernize HR experiences and outcomes.

Qualifications

Required:

  • Bachelor's degree in a STEM field (e.g., Computer Science, Engineering, Statistics, Data Science)
  • 2+ years building and delivering LLM/GenAI solutions with Claude/GPT(Codex)/Gemini-class models, including prompt/context design, tool/function calling, evaluation, and production integration.
  • 2+ years implementing RAG/retrieval (document processing, embeddings, vector/hybrid search) with enterprise governance controls.
  • 2+ years of modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals.
  • 2+ years building production, real-time inference services (API design, latency/performance, reliability patterns).
  • 2+ years leading platform/integration engineering across enterprise systems; strong API/integration experience (REST, GraphQL, event-driven, microservices, middleware).
  • 2+ years DevOps/DevSecOps experience (CI/CD, IaC such as Terraform/CloudFormation, Docker/Kubernetes, observability/monitoring).
  • 2+ years leading security/compliance efforts; familiarity with enterprise security controls (IAM, encryption, secrets, audit logging) and data/privacy (PII, retention, access controls); SOC 2/GDPR/HIPAA exposure a plus.
  • Ability to travel 0-25%, on average, based on client and project needs.
  • Must be legally authorized to work in the United states without the need for employer sponsorship, now or at any time in the future

Preferred:

  • Advanced degree (MS/PhD) and/or relevant certifications (cloud and AI/ML).
  • 2+ years of experience with Human Capital platforms and integrations (e.g., Workday, SAP SuccessFactors, Oracle HCM, Salesforce) and HR data domains.
  • 2+ years of experience operationalizing LLMOps/MLOps capabilities (evaluation, monitoring, governance workflows, model/prompt/version management).
  • 2+ years of cloud experience on AWS/Azure/GCP (one or more), including managed data platforms and scalable compute patterns.
  • 2+ years of experience with structured problem solving, translating business needs into requirements, acceptance criteria, and shippable increments.
  • 2+ years of experience with stakeholder communication: ability to explain AI/GenAI trade-offs (quality vs. latency vs. cost vs. risk) and document decisions.
  • 2+ years of experience collaborating across product, data science/ML, data engineering, platform, and security.
  • 2+ years of experience with treat testing, monitoring, and operational readiness as core responsibilities.
  • 2+ years of experience with ethics and privacy awareness being able to recognize consent/PII/bias boundaries and escalate appropriately.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $91,100 to $179,500.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Deloitte is committed to providing reasonable accommodations for people with disabilities. If you require a reasonable accommodation to participate in the recruiting process, please direct your inquiries to the Global Call Center (GCC) at USTalentCICInbox@deloitte.com.

For more information about Human Capital, visit our landing page at:https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-human-capital-consulting-jobs.html

#HCFY27  #HRSTFY27

Qualifications:

Position Summary

Our Deloitte Human Capital team transforms technology platforms, drives innovation, and helps make a significant impact on our clients' success. We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that power Human Capital AI products and analytics. You will work with an AI Data Engineer (data ingestion, curation, governance, platform foundations) and a Lead AI Solutions Architect (end-to-end solution architecture, integration patterns, non-functional requirements), partnering closely with product, data science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI solutions.

This role is hands-on and delivery-oriented: you will ship production pipelines and services that support model training, real-time inference, and LLM applications using Claude-, GPT/Codex-, and Gemini-class models, and more implemented with strong governance, observability, and cost/performance discipline.

Recruiting for this role ends on 08/30/2026.

Work you'll do

As an AI Engineer Consultant on the HC Forward team, you will design, build, and run the trusted, governed data + feature + retrieval layer used by AI/ML and GenAI solutions. You will deliver reproducible datasets and features, operationalize quality and lineage, and enable secure consumption patterns for both predictive ML and LLM-based experiences.

  • Partner with the Lead AI Solutions Architect and AI Data Engineer to translate Human Capital product needs into secure, scalable technical designs and delivered solutions (APIs, services, pipelines, containers/serverless) meeting availability, performance, and security expectations.
  • Build and operationalize LLM-enabled capabilities (e.g., copilots, HR knowledge assistants, summarization, policy Q&A) using Claude/GPT(Codex)/Gemini, including secure endpoints, tool/function calling, and reusable prompt/context patterns.
  • Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry.
  • Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills).

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to provide clear guidance to others

The team

HC Forward is a dedicated innovation partner accelerating the future of Human Capital by building market-aligned products, platforms, and services that apply AI, data, and engineering to modernize HR experiences and outcomes.

Qualifications

Required:

  • Bachelor's degree in a STEM field (e.g., Computer Science, Engineering, Statistics, Data Science)
  • 2+ years building and delivering LLM/GenAI solutions with Claude/GPT(Codex)/Gemini-class models, including prompt/context design, tool/function calling, evaluation, and production integration.
  • 2+ years implementing RAG/retrieval (document processing, embeddings, vector/hybrid search) with enterprise governance controls.
  • 2+ years of modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals.
  • 2+ years building production, real-time inference services (API design, latency/performance, reliability patterns).
  • 2+ years leading platform/integration engineering across enterprise systems; strong API/integration experience (REST, GraphQL, event-driven, microservices, middleware).
  • 2+ years DevOps/DevSecOps experience (CI/CD, IaC such as Terraform/CloudFormation, Docker/Kubernetes, observability/monitoring).
  • 2+ years leading security/compliance efforts; familiarity with enterprise security controls (IAM, encryption, secrets, audit logging) and data/privacy (PII, retention, access controls); SOC 2/GDPR/HIPAA exposure a plus.
  • Ability to travel 0-25%, on average, based on client and project needs.
  • Must be legally authorized to work in the United states without the need for employer sponsorship, now or at any time in the future

Preferred:

  • Advanced degree (MS/PhD) and/or relevant certifications (cloud and AI/ML).
  • 2+ years of experience with Human Capital platforms and integrations (e.g., Workday, SAP SuccessFactors, Oracle HCM, Salesforce) and HR data domains.
  • 2+ years of experience operationalizing LLMOps/MLOps capabilities (evaluation, monitoring, governance workflows, model/prompt/version management).
  • 2+ years of cloud experience on AWS/Azure/GCP (one or more), including managed data platforms and scalable compute patterns.
  • 2+ years of experience with structured problem solving, translating business needs into requirements, acceptance criteria, and shippable increments.
  • 2+ years of experience with stakeholder communication: ability to explain AI/GenAI trade-offs (quality vs. latency vs. cost vs. risk) and document decisions.
  • 2+ years of experience collaborating across product, data science/ML, data engineering, platform, and security.
  • 2+ years of experience with treat testing, monitoring, and operational readiness as core responsibilities.
  • 2+ years of experience with ethics and privacy awareness being able to recognize consent/PII/bias boundaries and escalate appropriately.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $91,100 to $179,500.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Deloitte is committed to providing reasonable accommodations for people with disabilities. If you require a reasonable accommodation to participate in the recruiting process, please direct your inquiries to the Global Call Center (GCC) at USTalentCICInbox@deloitte.com.

For more information about Human Capital, visit our landing page at:https://www2....


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