1304 Agentic Ai Software Engineer jobs in Dallas,TX

Bachelor's degree in computer science, electrical engineering, automation engineering, or equivalent
2+ years of PLC programming or automation engineering experience
Experience with PLC-controlled automation and issue diagnosis in a continuous process or production environment
Experience with robotics work cells and its control systems
Experience with Ladder Logic and structure programming from Siemens, Allen-Bradley, or Codesys PLCs
Experience with electrical theory, robotics, controls components, and automated equipment
PLC/PC controllers
Industrial networks such as Ethernet, ControlNet, DeviceNet, Profibus
Motor control systems
Servo drives
Frequency drives
Electrical distribution systems
Move up to 49lbs as well as stand and walk during shifts lasting up to 12 hours, with or without reasonable accommodation
Climb ladders and gangways safely and without limitation
Regular bending, lifting, stretching, and reaching both below the waist and above the head
Engage in full manual dexterity in both hands and wrists

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New, Posted 1 day ago

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Manager, AI Engineer

KPMG

Dallas, TX 75217

OnsiteHealth InsuranceRetirement Benefit

Minimum five years of recent professional experience in AI/ML, data engineering, or cloud solution engineering, with a minimum two years in a consulting or client-facing leadership role
Master's degree from an accredited college or university preferred; minimum of a Bachelor's degree from an accredited college or university in computer science, data science, engineering, or related field required
Professional certifications in cloud AI/ML platforms (for example: Azure AI Engineer Associate, AWS ML Specialty, Google Cloud ML Engineer) are a plus
Track record of delivering AI/ML solutions at enterprise scale, including integrations with core business systems with demonstrated ability to lead technical teams, manage deliverables, and build trusted client relationships
Hands-on expertise with at least two major cloud AI platforms (Azure AI/ML, AWS Bedrock/SageMaker, or Google Cloud Vertex AI)
Proficiency in Python (and/or other relevant languages) with strong experience in API development, microservices, containers, and CI/CD pipelines
Familiarity with MLOps frameworks (model monitoring, retraining pipelines, drift detection) and modern data engineering practices
Experience in building conversational AI/chatbots, generative AI use cases, or AI-powered automation solutions
Knowledge of AI security, data privacy, governance, and ethical AI frameworks
Strong problem-solving skills with the ability to translate complex technical concepts for executives
Excellent verbal and written communication skills, with experience creating client-facing deliverables
Willingness and ability to travel
Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future; KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity

SmartExplore AI is experimental.

New, Posted 14 hours ago

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Principal Engineer - Python API Development

Fidelity Investments

Euless, TX 76039

~ 28 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.