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# AI/ML Engineer

- **Company:** ConglomerateIT LLC
- **Location:** Dallas, TX, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Cloud Engineering, Continuous Integration, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Azure Machine Learning, SAS (Software), Data Streaming, Feature Store, Feature Engineering, Pytorch, Apache Spark, State Machines, Pandas, Core Data, Scikit Learn, AWS Data Analytics, Apache Kafka, Machine Learning Operations, Functional Programming, Model Explainability, Microservices
- **Published:** October 15, 2026
- **Apply:** https://www.careerjet.com/jobad/usd4267c438ae7248867ed1a31d9a8f693

## About the Role

* Hands-on experience building end-to-end ML pipelines, with solid skills in data prep, feature engineering, and core data engineering
 * Strong Python coding, using libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch
 * Practical work with AWS data tools such as S3, Glue, Athena, and Spark for processing large datasets
 * Deep knowledge of AWS ML services, mainly SageMaker, SageMaker Pipelines, Feature Store, and model deployment
 * A track record of building ML models using supervised and unsupervised methods, predictive analytics, and statistical techniques
 * Background in SAS for analytics and ML, including building, validating, and deploying models in large enterprise settings
 * MLOps know-how covering the full model lifecycle: CI/CD, monitoring, retraining, and supporting models in production
 * Ability to own technical design through build, deployment, and delivery with little supervision

Good to have:

 * Experience creating reusable ML platforms, feature stores, and company-wide AI/ML solutions
 * Familiarity with model explainability, Responsible AI, observability, and ML governance
 * Knowledge of real-time streaming and event-driven setups using Kafka, Kinesis, Lambda, and Step Functions
 * Experience with cloud-native APIs, microservices, and connecting enterprise apps on AWS