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# AI Engineer and Data Engineer

- **Company:** BMR Infotek
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Software Applications, Microsoft Azure, BigQuery, Cloud Computing, Continuous Integration, Data as a Services, Data Architecture, Data Validation, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Security, Data Warehousing, Database Design, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, SQL Databases, Workflow Management Systems, Enterprise Data Management, Pinecone, Google Cloud, Cloud Platform System, Data Ingestion, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Deep Learning, Model Validation, Llamaindex, Generative AI, Agentic-AI, Git, Containerization, Data Lakes, Pyspark, Scikit Learn, Information Technology, Deployment Automation, Weaviate, Google Bigquery, Apache Kafka, Machine Learning Operations, FAISS, Video Streaming, Restful APIs, Data Pipelines, Docker, Amazon Redshift, Databricks
- **Published:** October 20, 2026
- **Apply:** https://www.dice.com/job-detail/12078f21-9aa7-44fe-b23d-36731a2ceec8

## About the Role

* Strong proficiency in Python and SQL.
 * Experience with data engineering, ETL/ELT pipelines, and data transformation.
 * Knowledge of Machine Learning, Deep Learning, and AI application development.
 * Understanding of Generative AI, LLMs, RAG, and prompt engineering.
 * Experience with at least one cloud platform: AWS, Azure, or Google Cloud Platform.
 * Familiarity with Apache Spark, PySpark, and data orchestration tools.
 * Experience with data warehouses such as Snowflake, Databricks, Redshift, or BigQuery.
 * Knowledge of REST APIs, Git, CI/CD, and Docker.
 * Understanding of data modeling, database design, and scalable data architectures.
 * Strong analytical, problem-solving, and communication skills., * Experience with vector databases such as Pinecone, FAISS, or Weaviate.
 * Familiarity with LangChain, LlamaIndex, and AI agent development.
 * Experience with MLOps, MLflow, and AI model deployment.
 * Knowledge of streaming technologies such as Apache Kafka.
 * Experience building production-grade AI solutions integrated with enterprise data platforms., Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.

Candidates must be authorized to work in the United States without current or future employer sponsorship.

## Description

We are seeking a skilled AI & Data Engineer to design, develop, and maintain scalable data pipelines, cloud-based data platforms, and AI-powered applications. The ideal candidate will have a strong background in Python, SQL, ETL/ELT processes, cloud technologies, machine learning, and Generative AI.

The candidate will be responsible for building reliable data infrastructure, preparing high-quality datasets, and developing AI/ML solutions that support business intelligence, advanced analytics, and intelligent automation., * Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.

 * Build data ingestion, transformation, and processing solutions using Python and SQL.
 * Develop and optimize data warehouses, data lakes, and cloud-based data platforms.
 * Work with AWS, Azure, or Google Cloud Platform for data engineering and AI workloads.
 * Develop and deploy machine learning models and AI-powered applications.
 * Implement Generative AI solutions using Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG).
 * Work with AI/ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
 * Utilize Apache Spark, PySpark, and workflow orchestration tools such as Apache Airflow.
 * Work with Snowflake, Databricks, Amazon Redshift, or Google BigQuery.
 * Integrate AI models and data services using REST APIs and cloud-native technologies.
 * Implement data quality checks, model monitoring, and performance optimization.
 * Collaborate with data scientists, software engineers, and business stakeholders to deliver scalable solutions.
 * Follow best practices for data security, governance, model reliability, and deployment automation.