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# Senior ML Engineer

- **Company:** LMK Infotech
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Cloud Computing, Continuous Integration, Python (Programming Language), Tensorflow, Azure Machine Learning, Feature Engineering, Pytorch, Large Language Models, Scikit Learn, Machine Learning Operations, Software Version Control
- **Published:** May 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=df47d5c81fdfd296

## About the Role

Do you have experience in Production systems?, * 5+ years building and deploying ML models in production

 * Strong Python and ML framework experience (PyTorch, TensorFlow, or scikit-learn)
 * Cloud ML platform experience (SageMaker, Vertex AI, or Azure ML)
 * Solid understanding of MLOps - CI/CD for models, monitoring, and serving infrastructure
 * Comfort with messy real-world data and robust preprocessing pipelines
 * Ability to explain model trade-offs to non-technical stakeholders

## Description

You'll work directly with clients and cross-functional teams to build models that solve real business problems - demand forecasting, clinical document understanding, risk scoring, and intelligent process automation. Every model you build ships to production and creates measurable impact., * Design and implement end-to-end ML pipelines - ingestion, feature engineering, training, evaluation, and serving

 * Build and fine-tune models for NLP, structured prediction, and time-series forecasting
 * Deploy models to production with monitoring, drift detection, and automated retraining
 * Collaborate with data engineers on feature stores and training data pipelines
 * Evaluate and integrate LLM-based solutions where they provide clear value
 * Establish best practices for experiment tracking, model versioning, and reproducibility


## Related Videos

- [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python)
- [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)