Top 320 Linux integration engineer Jobs in Texas

Principal Engineer - Python API Development
Fidelity Investments —Westlake, TX
- $107,000 - $216,000 a year
- Full-time
- Azure
- Computer Science
- System design
- Loan repayment program
- Tuition reimbursement
Systems Engineer Integration and Test - Level 2
Lockheed Martin Corporation —Fort Worth, TX
- Full-time
- Computer science
- Computer Science
- Software troubleshooting
- Paid jury duty
- Paid holidays
Systems Software Engineer, (Linux, CUDA)
Micron —Richardson, TX
- Computer science
- Computer Science
- System design
- Employee stock purchase plan
- Paid holidays
Autonomous Robotics Software Engineer (Robotics / Autonomy / C++)
Advanced Robotics Group, LLC —Houston, TX
- Full-time
- Background check
- Computer Science
- 5 years
- Paid holidays
- Disability insurance
Quick Apply
8d
Senior Data Engineer with DevOps
CGI Group, Inc. —Dallas, TX
- $79,600 - $139,300 a year
- 5 years
- Writing skills
- DevOps
- Paid parental leave
- Paid holidays
5d
Software Engineer Principal (Capital Markets/Java)
PNC Financial Services Group —Dallas, TX
- $91,000 - $185,900 a year
- Full-time
- 5 years
- System design
- Office
- Wellness program
- Health savings account
5d
Embedded Engineer
Crescent Systems, Inc. —Richardson, TX
- From $90,000 a year
- Full-time
- 5 years
- Bachelor of Science
- C++
- Health savings account
- Relocation assistance
Quick Apply
Apps Dev Tech Lead Analyst - Vice President
Citi —Irving, TX
- $125,760 - $188,640 a year
- Full-time
- Computer science
- 6 years
- Computer Science
- Paid holidays
- Disability insurance
Software Engineer Lead - Data Product Organization (Cloudera Hadoop, Neo4j, Spark/PySpark)
PNC Financial Services Group —Farmers Branch, TX
- $55,000 - $138,000 a year
- Full-time
- Office
- 3 years
- Bachelor's degree
- Wellness program
- Health savings account
Lead DevSecOps Engineer
System One —Dallas, TX
- $170 an hour
- Windows
- Linux
- Senior level
- Health insurance
- Dental insurance
Quick Apply
6d
Remote Staff SW Engineer ($150-$240k) Ruby, Rails, AI
Everforth —Houston, TX
- $150,000 - $240,000 a year
- Full-time
- Remote
- US work authorization
- Fair chance
- Linux
- Dental insurance
- 401(k)
7d
Principal Software Engineer (Mission Systems) (Onsite)
Raytheon —McKinney, TX
- $107,500 - $204,500 a year
- Full-time
- System design
- 8 years
- Analysis skills
- Paid holidays
- Disability insurance
5d
RF Engineer Staff - Level 4
Lockheed Martin Corporation —Fort Worth, TX
- Full-time
- 11+ years
- Microsoft Office
- Bachelor's degree
- Paid jury duty
- Paid holidays
4d
Entry/Associate Electrical R&D Test Engineer
Saronic Technologies —Austin, TX
- Full-time
- Bachelor of Science
- Analysis skills
- Linux
- Paid parental leave
- Paid holidays
Systems Integration Engineer
Meta —Austin, TX
- $173,000 - $245,000 a year
- Bachelor of Science
- Engineering
- Java
Software Engineer - Simulation & Integration - Level 4
Lockheed Martin Corporation —Fort Worth, TX
- Full-time
- Laboratory
- Mid-level
- Analysis skills
- Paid jury duty
- Paid holidays
14d
OS/Software Engineer
Hewlett Packard Enterprise —Spring, TX
- $92,700 - $213,500 a year
- Full-time
- Computer Science
- Software troubleshooting
- System design
6d
Endpoint Automation Staff Engineer
GEICO —Dallas, TX
- $110,000 - $230,000 a year
- Full-time
- Computer science
- Computer Science
- Mid-level
- Health insurance
- Tuition reimbursement
System Integration and Test Engineer, Sr Stf. - level 5
Lockheed Martin Corporation —Fort Worth, TX
- Weekends as needed
- Windows
- Bachelor's degree
- Linux
- Paid jury duty
- Paid holidays
10d
Applied AI Software Engineer (Integration)
Lockheed Martin Corporation —Fort Worth, TX
- Full-time
- Computer Science
- Mid-level
- Java
- Paid jury duty
- Paid holidays

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linux integration engineer jobs near texas

Principal Engineer - Python API Development

Fidelity Investments -
Westlake, TX

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Job Details

Full-time
$107,000 - $216,000 a year

Benefits

Loan repayment program
Tuition reimbursement
Paid time off
Parental leave

Qualifications

Infrastructure as Code (IaC)
System design
Git
Scalable systems
Enterprise software systems development
SRE
Incident management operations support
Scalability
Cloud Architecture Design (Architecture design skills)
Linux
Cloud automation
Capacity planning
Distributed computing
DevOps automation
Design (software development lifecycle)

Full Job Description

Job Description:

Note: Fidelity will not provide immigration sponsorship for this position.

The Role:

As a Principal Engineer on the Enterprise AI/ML Platform team, you will tackle the most complex technical challenges involved in delivering machine learning at enterprise scale. You will design, build, and evolve reliable, secure, and cost‑efficient platform capabilities—from model packaging and serving to observability and lifecycle management—working closely with multiple teams to ensure these capabilities are practical, robust, and widely usable in production.

You will take a hands‑on role across enterprise repositories, improving shared services, CI/CD workflows, and infrastructure patterns where they have the greatest impact. This includes deep technical investigation of performance and scalability issues, such as tracking down bottlenecks in web services, analyzing system and application metrics, and optimizing GPU utilization, throughput, and resource efficiency across ML workloads.

The Expertise & Skills You Bring

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a closely related engineering discipline; 8+ years (typically 10+) building and operating production platforms and services at scale.
Deep software engineering expertise in Python and distributed systems, with a track record of building production‑grade services, libraries, and internal platforms. You model engineering excellence through clean designs, automated testing, and maintainable abstractions; Linux fluency and scripting are required.
Familiarity with Java or Groovy is a plus.
Knowledge or experience with GenAI Gateways or LiteLLM a big plus.
Cloud platform leadership (AWS)—hands‑on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt. Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial.
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads.
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines.
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus.
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes.
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units.
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production.
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services.
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability.
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform.
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale.
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap.

The Team

The Enterprise Data Science Platform, part of the Fidelity Data Architecture team within the Enterprise Technology business unit, is responsible for delivering scalable AI/ML capabilities across the organization. The team designs and builds advanced cloud-based, open-source, software platforms in close collaboration with Data Scientists, enabling the efficient packaging, deployment, and operation of AI/ML models at production scale.

In addition, the platform develops and maintains enterprise-grade gateways that allow teams across the company to securely discover, access, and consume AI/ML models. These gateways provide critical visibility into model usage and costs, while generating insights into model effectiveness, adoption patterns, and opportunities for continuous improvement.

Fidelity’s Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.

The base salary range for this position is $107,000-216,000 USD per year.

Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.

Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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Job Description:

The Expertise & Skills You Bring

Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines.

ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus.

Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes.

Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units.

Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production.

Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services.

Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability.

Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform.

The Team

Certifications:

Category: