Hi , Our client is looking for an Lead MLOps Engineer - Fraud Detection Platform for a project and below is the detailed requirement. Job Title : Lead MLOps Engineer - Fraud Detection Platform Location : Dallas, TX Prima…
Engineering & IT · Dallas, TX
Machine Learning Engineer jobs in Dallas, TX
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
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$43–$111/hr
Typical pay range
$148k
Median, full-time
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Title: Mid-Senior GenAI / Machine Learning Engineer (Telecom Domain) Location: Dallas, TX (Onsite 3 Days/Week)- Interview onsite final round Duration: Long Term Experience: 8+ Years Job Description: We are looking for a…
Must Have Technical/Functional Skills Databricks and Azure AI Foundry must-have; ML engineering/ Applied AI; production ML deployment; RAG and Agentic AI systems; Python; model serving frameworks and API development such…
Hi I hope you are doing well, AI/ML Engineer Dallas TX :: Malvern PA :: Onsite Responsibilities: Design, develop, and deploy scalable AI/ML and Generative AI solutions on AWS, leveraging services such as Amazon Bedrock,…
Class of Q1' 2026 supporting the Data Center Critical Facilities Engineer. The training will be on the cutting-edge of technology in a... ..., or HVAC and skilled Mechanical trades? Or desire to learn a new skill or trad…
$45 per hour
specialized data scraping workflows for real-world use cases. Mindrift is looking for highly skilled Senior Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows for real…
$37 per hour
Mindrift is looking for highly skilled Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows within our hybrid AI + human system. In this role, as an AI Pilot – that’s ho…
which employs over 30,000 people across 80+ locations globally. We are seeking an experienced Senior Generative AI / Machine Learning Engineer with 10+ years of experience to design, develop, and deploy enterprise AI sol…
solutions to address challenges faced by manufacturing and engineering teams. As a global organization, we strive to create solutions... ...capabilities across the organization. We're seeking a Machine Learning Engineer…
$60k - $120k
Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Compensation: $60.00 - $120,000.00 Work Model: Onsite – onsite Hours: 40.0 Security Clearance: None specified Overview Leave…
$157k - $234k
want to impact the world in a positive way. To learn more visit: You will.. - Design, develop, and implement the machine learning platform for the continuous... ...models. - Collaborate with data scientists and engineers…
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What machine learning engineers earn in Dallas
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $90k–$125k |
| Mid level | $60–$82 | $125k–$170k |
| Senior | $79–$111 | $165k–$230k |
National ranges — pay in Dallas typically tracks these.
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What employers ask for
The skills these listings keep naming
Python and software engineeringPyTorch or TensorFlowML fundamentals and evaluationModel serving and APIsMLOps (tracking, registries, CI)Docker and KubernetesData pipelines and feature storesLLM fine-tuning and RAG (a plus)Monitoring and drift detection
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Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through taking a model from prototype to production.
Cover data pipelines, training reproducibility, serving, and monitoring. Emphasize that the model is a small part of the system — that framing is the job.
How do you monitor a model in production?
Discuss input drift, prediction distributions, delayed labels, and business metrics — plus what triggers retraining. Mention that silent degradation is the default failure mode.
How would you reduce inference latency or cost for a large model?
Options include distillation, quantization, batching, caching, and smaller models. Frame it as measuring first, then choosing the cheapest acceptable quality tradeoff.
Tell me about a time a model failed in production. What happened?
A real story about skew, drift, or a data bug — with detection and prevention — is far more convincing than claiming smooth deployments.
How do you evaluate a model beyond accuracy?
Talk about the metric matching the business cost of errors, slicing by segment, and offline-online gaps. Naming a case where accuracy misled is a strong touch.
When would you fine-tune an LLM versus use retrieval or prompting?
Start cheap: prompting, then RAG for knowledge, fine-tuning for behavior and format. Cost and maintenance burden should drive the answer.
How do you make training reproducible?
Version code, data, and config; track experiments; pin environments. This is engineering discipline applied to ML, which is exactly the role.
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Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
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Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
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Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
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Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
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Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
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Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
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Where this role goes
Typical progression
01
ML Engineer
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Senior ML Engineer
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Staff ML Engineer
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ML Platform Lead
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