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# AI/ML Engineer

- **Company:** Resourcesoft, Inc.
- **Location:** The Woodlands, TX, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Business Software, Continuous Integration, Data Warehousing, Python (Programming Language), Machine Learning, Tensorflow, Search Technologies, Data Streaming, Digital Twin, Google Cloud, Feature Engineering, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Llamaindex, Generative AI, Agentic-AI, Containerization, Scikit Learn, Kubernetes, Low Latency, Machine Learning Operations, Drift Detection, Software Version Control, Data Pipelines, Databricks
- **Published:** November 4, 2026
- **Apply:** https://www.dice.com/job-detail/849f0ce7-8936-4e22-80eb-8d70e263fb7a

## About the Role

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

## Description

* Design, build, evaluate, and deploy scalable machine learning and Generative AI solutions for enterprise use cases.
 * Develop RAG pipelines, prompts, embeddings, vector search, guardrails, evaluation workflows, and AI agents.
 * Build batch and streaming data pipelines, feature engineering processes, training jobs, and production inference services.
 * Implement MLOps practices covering experimentation, CI/CD, model versioning, monitoring, quality checks, and drift detection.
 * Integrate AI/ML solutions with cloud platforms, APIs, event streams, data warehouses, BI tools, and business applications.
 * Optimize models for accuracy, latency, throughput, cost, security, reliability, and measurable business outcomes.