1321 Ai Application Engineer jobs in Dallas,TX

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Salesforce Application Architect

Keurig Dr Pepper

Frisco, TX 75033

~ 29 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Master's or Bachelor's degree in related field (e.g., Computer Science, Computer Engineering, Electrical Engineering, Information Technology, System Analysis, etc.) or equivalent combination of education and work experience
10 – 12+ years of experience in IT and business/industry including strategic and operations planning
10+ years of experience in at least three disciplines, such as business, information, solution, or technical architecture, application development, middleware, information analysis, database management, or operations
Experience in working in multiple, diverse technologies and processing environments.
3–5 years of IT experience including Salesforce architecture, design, and delivery.
Knowledge of SDLC methodologies with experience delivering Salesforce solutions in Agile and DevOps environments
Proven ability to translate strategic vision into executable Salesforce roadmaps.
Ability to lead cross-functional teams and communicate effectively with business stakeholders.

SmartExplore AI is experimental.

New, Posted 11 hours ago

Application Engineer - AI Trainer

DataAnnotation

Dallas, TX 75217

Flexible ScheduleBilingual Preferred

New, Posted 1 day ago

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Developer/Sr Developer, IT Applications

American Airlines

Fort Worth, TX 76101

New, Posted 1 day ago

Remote Online Tasks – Earn Up to $1,500/month Playing Games

Free Cash

Dallas, TX 75396

$1,500/mo

Remote

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Principal Engineer - Python API Development

Fidelity Investments

Southlake, TX 76092

~ 36 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.