Associate Data Engineer

Ryan•Plano, TX

20h•Hybrid

About The Position

The Associate AI/ML Engineer, Data Engineering will be responsible for the design, development, and deployment cutting-edge artificial intelligence (AI) and machine learning (ML) models that solve complex business problems and enhance operational efficiency. This role combines deep knowledge of AI techniques with practical machine learning engineering expertise. The ideal candidate will have proficiency in building and deploying models using modern AI/ML frameworks, working with cloud-based platforms, integrating AI solutions into production environments, building MLOps pipelines, and writing production quality code in Python. The candidate will collaborate with data scientists, data engineers, and software developers to create scalable and maintainable AI/ML systems.

Requirements

Bachelor’s and/or master’s degree in Computer Science or related study.
1-2 years of experience developing data technologies.
1-2 years of experience deploying ETL solutions in production environments.
1-2 years of experience with cloud-based data services, preferably in AWS or Azure.
1-2 years of experience developing Python, Scala, Java, .Net or similar solutions in a backend or data wrangling capacity.
1-2 years of experience in mixed Windows/Linux environments.
Intermediate knowledge of Microsoft Project, Word, Excel, Access, PowerPoint, Outlook, and Internet navigation and research.
Valid driver’s license required.

Nice To Haves

Proficiency in building and deploying models using modern AI/ML frameworks.
Working with cloud-based platforms.
Integrating AI solutions into production environments.
Building MLOps pipelines.
Writing production quality code in Python.
TensorFlow, PyTorch, Keras, XGBoost, Scikit-learn and MLFlow.
NLP experience includes NLTK, BERT, GPT.
Azure ML, Azure Document Intelligence Databricks, and/or AWS SageMaker.
Software best practices for flexible, extensible microservice application architectures.
Resilient, robust production-grade code that runs in a cloud environment in container services like AKS, EKS, ECS, Container Apps in Azure and/or AWS.
MLOps best practices.
MLFlow.
GenAI techniques.
Agentic flows and leverage RAG solutions.
Similarity / RAG basic and advanced patterns, and agentic flows.
Langchain, Ollama Llamaindex, Langroid, CrewAI, VanniAI.
Vector DBs like Milvus, OpenSearch, Azure AI Search, and PGVector.
AWS, Azure, SageMaker, Azure Document Intelligence, Azure ML and/or Databricks.
Cost savings trade-offs for training and inference and leveraging serverless soutions.
Generative AI, reinforcement learning, and neural network architectures (e.g., CNNs, RNNs, Transformers).
Model monitoring, performance tracking, and continuous improvement strategies.
Model and data drift.
Generative AI, reinforcement learning, and neural network architectures (e.g., CNNs, RNNs, Transformers).
MLOps practices to automate deployment, monitoring, and retraining in Databricks using MLFlow.

Responsibilities

Use a variety of programming languages and tools to develop, test, and maintain data pipelines within the Platform Reference Architecture.
Working directly with management, product teams and practice personnel to understand their platform data requirements
Maintaining a positive work atmosphere by behaving and communicating in a manner that encourages productive interactions with customers, co-workers and supervisors
Developing and engaging with team members by creating a motivating work environment that recognizes, holds team members accountable, and rewards strong performance
Fostering an innovative, inclusive and diverse team environment, promoting positive team culture, encouraging collaboration and self-organization while delivering high quality solutions
Collaborating on an Agile team to design, develop, test, implement and support highly scalable data solutions
Collaborating with product teams and clients to deliver robust cloud-based data solutions that drive tax decisions and provide powerful experiences
Analyzing user feedback and activity and iterate to improve the services and user experience
Design, build, and optimize machine learning models and AI solutions using techniques such as supervised/unsupervised learning, deep learning, natural language processing (NLP), and computer vision.
Use frameworks such as TensorFlow, PyTorch, Keras, XGBoost, Scikit-learn and MLFlow.
NLP experience includes NLTK, BERT, GPT.
Ensure the models can be deployed and scaled in cloud environments like Azure ML, Azure Document Intelligence Databricks, and/or AWS SageMaker.
Have solid understanding in software best practices for flexible, extensible microservice application architectures that reside in an overall dynamic distributed system.
Understand how to implement resilient, robust production-grade code that runs in a cloud environment in container services like AKS, EKS, ECS, Container Apps in Azure and/or AWS and interfaces with other cloud and application services.
Develop and maintain scalable AI/ML pipelines, from data ingestion and preprocessing to model training, validation, and deployment using MLOps best practices.
Have hands-on experience with technologies like MLFlow.
Apply GenAI techniques to real-world business cases, developing models that generate data-driven insights, automate processes, and enhance operational efficiency.
Apply agentic flows and leverage RAG solutions where appropriate.
Have a firm understanding of similarity / RAG basic and advanced patterns, and agentic flows.
Be hands-on with fine-tuning and libraries like Langchain, Ollama Llamaindex, Langroid, CrewAI, VanniAI.
Understand and have experience with Vector DBs like Milvus, OpenSearch, Azure AI Search, and PGVector.
Deploy machine learning and AI models in production environments using cloud platforms like AWS, Azure, using SageMaker, Azure Document Intelligence, Azure ML and/or Databricks ensuring robust integration with existing systems.
Understand cost savings trade-offs for training and inference and leveraging serverless soutions.
Work on cutting-edge AI initiatives, including generative AI, reinforcement learning, and neural network architectures (e.g., CNNs, RNNs, Transformers), applying them to real-world use cases.
Implement model monitoring, performance tracking, and continuous improvement strategies, ensuring that AI/ML models maintain accuracy, performance, and scalability over time.
Understand how to handle model and data drift.
Partner with data scientists, data engineers, software developers, and product managers to understand business objectives, define AI/ML solutions, and deliver impactful results.
Build infrastructure for deploying and managing machine learning models at scale, incorporating MLOps practices to automate deployment, monitoring, and retraining in Databricks using MLFlow.