> Markdown version of [/jobs/ext/4555945-application-azure-engineer](https://www.wearedevelopers.com/jobs/ext/4555945-application-azure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Application/Azure Engineer

- **Company:** Resourcesoft, Inc.
- **Location:** Bethesda, MD, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Warehousing, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Search Technologies, Digital Twin, Google Cloud, Enterprise Software Applications, Feature Engineering, Pytorch, LangChain, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Llamaindex, Generative AI, Agentic-AI, Containerization, Scikit Learn, Kubernetes, Machine Learning Operations, Tools for Reporting, Drift Detection, Software Version Control, Data Pipelines, Databricks
- **Published:** November 4, 2026
- **Apply:** https://www.dice.com/job-detail/84156e71-286a-41d5-a6b8-68b2f13d4105

## About the Role

* 2 years or more of experience developing and deploying production AI/ML and Generative AI solutions using Python.
 * Proficiency in Python, LLMs, RAG, prompt engineering, embeddings, vector databases, and machine learning frameworks.
 * Experience with PyTorch, TensorFlow, scikit-learn, Transformers, LangChain, LlamaIndex, and model evaluation.
 * Experience with MLOps, LLMOps, CI/CD, containerization, model versioning, drift detection, monitoring, and observability.
 * Experience with AWS, Azure, Google Cloud Platform, Databricks, Kubernetes, data pipelines, digital twins, predictive maintenance, and agentic AI.
 * Excellent verbal and written communication skills

## Description

* Engineer, evaluate, and deploy scalable machine learning and Generative AI solutions for enterprise applications.
 * Build RAG workflows, prompts, embeddings, vector search, guardrails, evaluation pipelines, and agentic AI solutions.
 * Develop batch and streaming pipelines, feature engineering workflows, training processes, and production inference services.
 * Establish MLOps and LLMOps pipelines for experimentation, deployment, version control, monitoring, and model quality.
 * Connect AI/ML services with cloud platforms, APIs, event streams, data warehouses, analytics tools, and enterprise systems.
 * Improve model accuracy, performance, scalability, security, reliability, cost efficiency, and production effectiveness.