863 Applications Engineer jobs in Mckinney,TX

University degree in technical writing, English, journalism, or communications
Native-level proficiency in English, both verbal and written
Strong working knowledge of all Adobe Creative Cloud applications, including InDesign, Photoshop, Acrobat Pro, and Illustrator
Strong working knowledge of all Microsoft Office applications
Familiar with content development for instructional material
Technically oriented; understand, articulate and be able to write technical subjects
Exceptional professional written and oral communication skills
Analyze content/needs; structure and design curricula
Experience working with complex concepts and translating them into concise, clear language
Ability to quickly grasp complex technical concepts and make them easily understandable
Take a raw copy of a document, format, and organize the document
Research, prepare and edit complex technical documents
Experience in creating high-quality documentation with attention to formatting, voice, and audience
Able to work under the pressures of deadlines and other demands
Experience handling multiple projects, and time management to meet project deadlines
Teamwork and collaboration skills
This position is not a remote position
Experience content/needs; structure and design curricula

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Embedded Linux Software Engineer

Planmeca USA

Dallas, TX 75215

Flexible Schedule

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IT Solution Architect: Human Capital & Talent Management

Coca Cola Southwest Beverages

Dallas, TX 75240

New, Posted 19 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Lewisville, TX 75029

~ 31 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

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
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
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 base salary range for this position is $107,000-216,000 USD per year.
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.