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Mlops Data Engineer Jobs (NOW HIRING)

Applied Aerospace & Defense

Data Engineer

Newington, CT · On-site

$114K - $136K/yr

In alignment with current industrys best practices, this role integrates advanced data engineering, software development, and machine learning operations (MLOps) to deliver secure, scalable, and high ...

Applied Aerospace & Defense

Data Engineer

Newington, CT

$114K - $137K/yr

In alignment with current industry?s best practices, this role integrates advanced data engineering, software development, and machine learning operations (MLOps) to deliver secure, scalable, and ...

Alpha Consulting Corp.

Data Engineer

Chicago, IL · On-site

$118K - $141K/yr

Alpha Consulting Corp. is seeking a highly skilled MLOps Engineer / Python Developer with expertise in building and maintaining scalable data and machine learning pipelines. The role involves ...

Incedo Inc

Hadoop Data Engineer

Dallas, TX · On-site

$113K - $136K/yr

Sr. Feature Engineer Dallas,TX/ Pittsburgh, PA / Clevland OH Tech: Data Engineering/Pipeline MLOps Engineering/Pipeline OpenShift Git Linux Programming language: Python SQL Spark Hive Title Skillsets ...

Qualysoft

Erwartungsmanagement Anforderungen Mehrjahrige Erfahrung als MLOps Engineer, ML Engineer oder Data ... Engineer Sehr gute Kenntnisse in Kubernetes-/OpenShift-basierten Umgebungen Erfahrung mit ML ...

SOSi

Data Engineer

Redstone Arsenal, AL · On-site

$116K - $140K/yr

Overview SOS International LLC (SOSi) is seeking Data Engineers to join our analytics team working on an innovative MLOps workload leveraging cutting-edge technologies and supporting a government ...

Berkley

Databricks Data Engineer

Manassas, VA

$114K - $137K/yr

MLOps & ML-Enabled Data Pipelines * Partner with data scientists and data engineers to create feature pipelines, model training pipelines, and production scoring pipelines. * Deploy and ...

Berkley

Databricks Data Engineer

Manassas, VA · On-site

$114K - $137K/yr

MLOps & ML-Enabled Data Pipelines * Partner with data scientists and data engineers to create feature pipelines, model training pipelines, and production scoring pipelines. * Deploy and ...

SOSi

Data Engineer

Redstone Arsenal, AL · On-site

$116K - $140K/yr

Overview SOS International LLC (SOSi) is seeking Data Engineers to join our analytics team working on an innovative MLOps workload leveraging cutting-edge technologies and supporting a government ...

Berkley

Databricks Data Engineer

Manassas, VA · On-site

$114K - $137K/yr

MLOps & ML-Enabled Data Pipelines * Partner with data scientists and data engineers to create feature pipelines, model training pipelines, and production scoring pipelines. * Deploy and ...

ClifyX

Python AI/GenAI developer

Denver, CO · On-site

$51.75 - $71.25/hr

... AI/GenAI developer. The role involves working with Google technologies, local LLMs, vector ... MLOps data models. Responsibilities : • Exposure to Google technologies - Google ADK, Google ...

Ford Motor Company

Data Engineer

Dearborn, MI · On-site

$105K - $126K/yr

... MLOps) pipelines primarily within cloud environments like Google Cloud Platform (GCP) and Amazon ... Data engineering, data product development and software product launches * At least three of the ...

Staffingine LLC

Data Engineer

Redmond, WA · On-site

$128K - $154K/yr

Exposure to DataOps or MLOps practices is a plus. * Azure Data Engineer or related Microsoft certification preferred.

1 point system

Data Engineer

Los Angeles, CA · On-site

$60/hr

Job Title Data Engineer Client Confidential Location Los Angeles, CA (5 days - Onsite) Type of Hire ... Experience with cloud-based environments (AWS, GCP, Databricks) and MLOps practices * Strong ...

PCX Aerosystems

Data Engineer

Newington, CT · On-site

$114K - $136K/yr

In alignment with current industry's best practices, this role integrates advanced data engineering, software development, and machine learning operations (MLOps) to deliver secure, scalable, and ...

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All JobsMlops Data Engineer Jobs

Mlops Data Engineer information

See salary details

$44.5K

$129.7K

$177.5K

How much do mlops data engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for mlops data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

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.
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Mlops Data Engineer jobs near you
Infographic showing various Mlops Data Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

GCP Data Engineer with AI/ML Integration & MLOps

Tror AI for everyone

Irving, TX • On-site

Contractor

Re-posted 11 days ago

Job description

Role : GCP Data Engineer with AI/ML Integration & MLOps

Location : Irving, Texas 75039  (100% onsite)

Hire type : Contract

Interview Mode : 1 Video interview and client interview will be In person 

Overview

We are seeking a GCP Data Engineer with deep, hands-on architectural and development

experience in Google Cloud Platform’s big data ecosystem. You will be responsible for

designing, building, and optimizing a modern data lakehouse architecture. Your primary focus

will be leveraging BigLake, BigQuery, Google Cloud Storage (GCS), and Vertex AI to create

seamless, scalable data pipelines and machine learning integrations that drive business

intelligence and predictive analytics.

Key Responsibilities

  • Lakehouse Architecture & Development:
  • Architect and maintain a scalable data lakehouse using Google Cloud Storage
  • (GCS) as the foundational data lake and BigLake to unify data warehouses and data lakes.
  • Implement fine-grained security (row-level and column-level access controls) and
  • data governance across open file formats (Parquet, Iceberg, ORC) using BigLake.
  • Data Warehousing & Optimization:
  • Design and manage complex, highly scalable data models within Big Query.
  • Perform deep performance tuning and cost optimization of Big Query jobs utilizing
  • clustering, partitioning, materialized views, and slot capacity management.

AI/ML Integration & MLOps:

  • Collaborate with Data Scientists to operationalize machine learning models using
  • Vertex AI.
  • Build robust data pipelines to feed Vertex AI Feature Store, manage model
  • training workflows and deploy ML models into production.
  • Utilize Big Query ML (BQML) for in-database predictive modeling and analytics
  • where appropriate.

Data Pipeline Engineering:

  • Design, develop, and orchestrate batch and streaming data pipelines (using tools
  • like Dataflow, Dataproc, or Cloud Composer/Airflow) to ingest data from diverse
  • sources into GCS and BigQuery.
  • Data Governance & Best Practices:
  • Establish data lifecycle management policies in GCS.
  • Ensure data quality, reliability, and security compliance across the entire GCP big
  • data stack.
  • Mentor junior engineers and lead code/architecture reviews.

Required Qualifications

  • Experience: 5+ years of dedicated Data Engineering experience, with at least 3+ years
  • focused exclusively on the Google Cloud Platform (GCP).

Deep GCP Big Data Expertise:

  • BigQuery: Expert-level knowledge of BigQuery architecture, advanced SQL,
  • analytical functions, query profiling, and optimization techniques.
  • BigLake: Proven experience utilizing BigLake for multi-cloud or lakehouse
  • architectures, managing open-source formats (e.g., Apache Iceberg/Parquet),
  • and enforcing unified security policies.
  • GCS: Deep understanding of GCS storage classes, object lifecycle management,
  • and optimizing GCS for big data workloads.
  • Vertex AI: Hands-on experience with Vertex AI pipelines, endpoints, feature
  • stores, or deploying ML models into scalable data environments.
  • Programming Skills: Advanced proficiency in Python and SQL. Familiarity with Java,
  • Scala, or Go is a plus.
  • Data Orchestration & CI/CD: Experience with orchestration tools (e.g., Apache Airflow,
  • Cloud Composer) and modern CI/CD pipelines (e.g., GitHub Actions, Terraform, Cloud
  • Build).

Preferred/Bonus Qualifications

  • GCP Certifications: Google Cloud Certified - Professional Data Engineer or
  • Professional Machine Learning Engineer.