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Contract Data Platform Engineer Jobs in Reston, VA

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations ...

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations ...

GDIT

Platform Engineer

Falls Church, VA

$111.16K - $150.39K/yr

... enterprise data, tools, and capabilities. Platform Engineer * Supports the integration ... configuration, and sustainment of Advana platform tools and services across Unclassified, Secret ...

Booz Allen Hamilton

M365 Platform Engineer

Mclean, VA

$86.80K - $198K/yr

Experience deploying Microsoft Purview solutions such as Sensitivity Labels, Data Lifecycle ... as well as contract-specific affordability and organizational requirements. The projected ...

Booz Allen Hamilton

M365 Platform Engineer

Mclean, VA

$86.80K - $198K/yr

Experience deploying Micro sof t Purview solutions such as Sensitivity Labels, Data Lifecycle ... as well as contract-specific affordability and organizational requirements. The projected ...

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Contract Data Platform Engineer information

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$46.3K

$135K

$184.7K

How much do contract data platform engineer jobs pay per year?

As of May 31, 2026, the average yearly pay for contract data platform engineer in Reston, VA is $134,951.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,100.00 and $143,000.00 per year, depending on experience, location, and employer.

What is the difference between Contract Data Platform Engineer vs Contract Data Engineer?

AspectContract Data Platform EngineerContract Data Engineer
Primary FocusBuilding and maintaining data infrastructure and platformsDeveloping and optimizing data pipelines and workflows
Skills & CertificationsCloud platforms, data architecture, scripting, certifications like AWS or GCPSQL, ETL tools, programming languages, data modeling
Work EnvironmentCollaborates with data platform teams, cloud environments, infrastructure focusWorks on data pipelines, analytics, and data processing tasks
Industry UsageUsed in organizations with complex data infrastructure needsCommon in data-driven companies focusing on data analysis and reporting

The Contract Data Platform Engineer primarily focuses on designing and maintaining data infrastructure and platforms, often working with cloud services and architecture. In contrast, the Contract Data Engineer concentrates on developing data pipelines and processing workflows. Both roles require strong data skills, but their focus areas differ, making them suitable for different project needs within data teams.

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Contract Data Platform Engineer jobs near you

MLOps Platform Engineer

Interon IT Solutions

Reston, VA • On-site

Contractor

Posted 28 days ago

Job description

#W2 only
 
Job title: MLOps Platform Engineer 
Location: Reston VA - In person interviews so need Local In EAST coast only​
Description: 
MLOps Platform Engineer 
The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps 
Platform Engineer to design, build, and support enterprise-grade machine learning operations 
capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model 
development and deployment across our cloud and container platforms. 
This is a hands-on engineering role requiring strong expertise in AWS, Kubernetes (EKS), 
CI/CD automation, containerization, and ML platform operations. The ideal candidate will have 
solid engineering fundamentals combined with practical knowledge of ML workflows, 
deployment patterns, and platform reliability. 
Key Responsibilities 
Platform Engineering & Operations  
· Engineer, manage, and support MLOps platform components across AWS and EKS-based 
environments. 
· Oversee deployment, configuration, and operation of infrastructure used for ML training, batch 
inference, and real-time model serving. 
· Ensure platform availability, resilience, and performance across dev, test, and production 
environments. 
· Implement role-based access controls (RBAC), network policies, and scalable namespace 
designs within EKS. 
Model Deployment & CI/CD Automation 
· Build and support CI/CD pipelines (GitLab) for model packaging, container image builds, 
vulnerability scanning, and automated deployment flows. 
· Enable standardized model release processes including environment promotion, versioning, and 
rollback workflows. 
· Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments. 
Container & Kubernetes Workloads 
· Design and manage EKS workloads supporting containerized ML jobs and microservices. 
· Implement auto-scaling, resource quotas, cluster optimization, and multi-tenant workload 
isolation. 
· Support GPU and CPU-based training/inference workloads. 
Monitoring, Observability & Optimization 
· Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, 
and platform components. 
· Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML 
workloads. 
· Perform incident response, root cause analysis, and long-term remediation planning. 
Collaboration & Enablement 
· Partner with Data Scientists, ML Engineers, and application teams to operationalize end-to-end 
machine learning solutions. 
· Provide technical guidance on best practices for ML model lifecycle management, deployment 
patterns, and scalable architectures. 
· Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases. 
--- 
Required Qualifications 
· 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, 
CloudWatch, and ECR. 
· Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS). 
· Proficiency in containerization (Docker) and orchestration concepts. 
· Strong programming/scripting experience in Python and Bash. 
· Experience building and managing CI/CD pipelines (GitLab or equivalent). 
· Familiarity with machine learning workflows, including training, inference, and model 
monitoring. 
· Experience with infrastructure-as-code (Terraform or CloudFormation). 
· Experience supporting production platforms, including incident management and root cause 
analysis.